{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "    print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764175e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 1e-8\n",
    "print 'Testing conv_forward_naive'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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PfXB2JdF8v5SIfg+i3f2yfN1vQIgUmPnx/M6Nr4UET3gCwHdB5twfL1WHe67PIKJfgbxL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/AAAg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9TrcEUwUJbllsLaN1b8yIiw+cn3YhQAW+PQP9/ft3yjXO4lBnqfTdKPhsg6sotFTSBnvmHi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Htfyu+3auFsPUQrcWZORw3m63s4AvcSPOuVnYjjFOadSPh7UWmrTGUNa1BhlyrbZeieZS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\njlkvencYjtPZgrbagGsZdps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1WK7c91Jkk1ZC5Fh5Ntm3TY9henxtn3+tHluDyTXacv0a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d7y3H8rSfR/69JFL94H6Hzf0rV2dr27KwiE8B+r1ao15+z1ehwfHzOfz7m5ueHq6qpl2oGtOdU0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\nfOt1arUKA4VBfu8f/n08TefazZtEDZ3p6WmazSbZXJZSqURpt8jPfek5/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RwZGeHIkSMM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\n3Lt3j9///d8noil5VCoV5ubm6A57E6h+yM2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Me/fucu3adTqdDplMhnA4TLFcYnl5mVvX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\nttEbo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+yHcRhtx1ZHROzHO2nsGM9ha+XYSCsq2Tn5TBzkcrmMxWLZhKmem5vD6/WKXglO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\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EpYLHtgjfB/cbXMAEaZAFSNN8JEYWZc/zTkXeJTIpi6E4LWJISMXsIL9cKoXLIiqLoMia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\nH6zVaMPLvzfLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a29caae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d/2:-d/2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.48340542384e-09\n",
      "dw error:  5.88236983542e-10\n",
      "db error:  1.06658018896e-11\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-9'\n",
    "print 'Testing conv_backward_naive function'\n",
    "print 'dx error: ', rel_error(dx, dx_num)\n",
    "print 'dw error: ', rel_error(dw, dw_num)\n",
    "print 'db error: ', rel_error(db, db_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print 'Testing max_pool_forward_naive function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27563347202e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print 'Testing max_pool_backward_naive function:'\n",
    "print 'dx error: ', rel_error(dx, dx_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 5.089387s\n",
      "Fast: 0.012292s\n",
      "Speedup: 414.043700x\n",
      "Difference:  3.29721453362e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 6.437567s\n",
      "Fast: 0.007233s\n",
      "Speedup: 890.038995x\n",
      "dx difference:  1.09613020945e-10\n",
      "dw difference:  8.5744825321e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing conv_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'Difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting conv_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)\n",
    "print 'dw difference: ', rel_error(dw_naive, dw_fast)\n",
    "print 'db difference: ', rel_error(db_naive, db_fast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.004839s\n",
      "fast: 0.001842s\n",
      "speedup: 2.626974x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 1.180283s\n",
      "speedup: 76.531901x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing pool_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'fast: %fs' % (t2 - t1)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting pool_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  2.22941423977e-08\n",
      "dw error:  3.86383761594e-09\n",
      "db error:  2.83589046945e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu_pool'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  2.4138248738e-09\n",
      "dw error:  1.22965398115e-09\n",
      "db error:  9.20906216162e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258672368\n",
      "Initial loss (with regularization):  2.50908461642\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (no regularization): ', loss\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (with regularization): ', loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.685440e-03\n",
      "W2 max relative error: 2.187680e-03\n",
      "W3 max relative error: 3.409845e-05\n",
      "b1 max relative error: 1.362410e-04\n",
      "b2 max relative error: 6.072979e-07\n",
      "b3 max relative error: 1.721728e-09\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 20) loss: 2.428154\n",
      "(Epoch 0 / 10) train acc: 0.180000; val_acc: 0.142000\n",
      "(Iteration 2 / 20) loss: 3.356089\n",
      "(Epoch 1 / 10) train acc: 0.160000; val_acc: 0.119000\n",
      "(Iteration 3 / 20) loss: 5.042654\n",
      "(Iteration 4 / 20) loss: 2.002389\n",
      "(Epoch 2 / 10) train acc: 0.200000; val_acc: 0.100000\n",
      "(Iteration 5 / 20) loss: 2.094873\n",
      "(Iteration 6 / 20) loss: 2.325239\n",
      "(Epoch 3 / 10) train acc: 0.260000; val_acc: 0.153000\n",
      "(Iteration 7 / 20) loss: 2.241239\n",
      "(Iteration 8 / 20) loss: 2.069478\n",
      "(Epoch 4 / 10) train acc: 0.290000; val_acc: 0.150000\n",
      "(Iteration 9 / 20) loss: 2.136681\n",
      "(Iteration 10 / 20) loss: 1.775010\n",
      "(Epoch 5 / 10) train acc: 0.440000; val_acc: 0.163000\n",
      "(Iteration 11 / 20) loss: 1.661116\n",
      "(Iteration 12 / 20) loss: 1.536272\n",
      "(Epoch 6 / 10) train acc: 0.480000; val_acc: 0.167000\n",
      "(Iteration 13 / 20) loss: 1.616086\n",
      "(Iteration 14 / 20) loss: 1.504754\n",
      "(Epoch 7 / 10) train acc: 0.560000; val_acc: 0.193000\n",
      "(Iteration 15 / 20) loss: 1.575009\n",
      "(Iteration 16 / 20) loss: 1.536015\n",
      "(Epoch 8 / 10) train acc: 0.650000; val_acc: 0.201000\n",
      "(Iteration 17 / 20) loss: 1.158443\n",
      "(Iteration 18 / 20) loss: 0.915840\n",
      "(Epoch 9 / 10) train acc: 0.740000; val_acc: 0.187000\n",
      "(Iteration 19 / 20) loss: 0.932950\n",
      "(Iteration 20 / 20) loss: 0.880372\n",
      "(Epoch 10 / 10) train acc: 0.720000; val_acc: 0.156000\n"
     ]
    }
   ],
   "source": [
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TpjXU19fnXKUkSVLtGoczVIlC4SYaG1exbNl5eRcjjapFiy4ph6nZlMIU2JhFkiRp5Iy7\nQDVlykdpa9vsN/MaF9at20hv76xBr6u2xizu75IkSbVo3C35u+GGL9DU1JR3GdKoSynR0zOJXTNT\nA+1qzJLXXsJisciiRZewbt1GenomUVe3kzlzZrB8+fl+4SFJkmrCuAtU0ngREdTV7QQSg4eqfBuz\nuL9LkiSNBeNuyZ80nsyZM4NCYf2g1+XdmMX9XZIkaSwwUElj2PLl59PYuJJC4SZKM1VQLY1Zaml/\nlyRJ0lAMVNIYVl9fz6ZNa2hr28z06Sczdeo8pk8/OffGLPuzv0t75r+RJEn5cg+VNMbV19fT3r6U\n9nZybUDRX7Xv76p2NvOQJKl6OEMljYJqnTWopoBSzfu7qllfM4+Ojha2bbuV++//Ntu23UpHRwst\nLfMpFot5lyhJ0rhioJJGSLFYZMGCJTQ0zGTatFNpaJjJggVL/IA7hGre3zVQNQVkm3lIklRdDFTS\nCHDWYP9V6/6uPtUakG3mIUlSdXEPlTQCdp816NM3a5BYvHgF7e1L8yqvalXj/i6o3nNk1cLJmiVJ\nGm9qcoYqIv4pIn4YEX+KiIci4psR8cq861JlVdMyLGcNhq+aAkC1LqvbvZnHYGzmIUlSpdVkoAJO\nAD4HvAmYCdQBt0TEwblWpVFXjcuwbAE+9lRzQLaZhyRJ1aUml/yllP5P/98j4izgD0Az8IM8atLo\nq9ZlWLYAH1uqfVnd8uXns2HDfLq7U78ZtEShcHO5mceaitckSdJ4VqszVAM9n9Kn2UfyLkSjp1qX\nYYGzBmNJtS+rq/ZmHpIkjTc1H6ii9KnmX4EfpJTuzrsejZ5qXoZVSy3AtXfVHpD7mnls3Xorv/3t\nt9i69Vba25capiRJykFNLvkb4DLgVcCMfbnxwoULmTx58m7HWltbaW1tHYXSNFKqfRlW36zB4sUr\nWLt2JT09E6mre5y5c2ewbJmzBrWmlpbVuZRUkqQ96+zspLOzc7djO3bsGLHxo5Y3ykfE54E5wAkp\npfv2ctsmYMuWLVtoamqqSH0aWQ0NM9m27VaG2qc0ffpfs3Xrdypd1qBsW137isViOSBvHBCQzzMg\nS5JU47q6umhubgZoTil1DWesmp2hKoepecBb9xamNDbMmTODjo71A871VFINy7D6M0zVvmo9R5Yk\nSaouNbmHKiIuA94HvBfYGRFHli8H5VyaRpH7lJQXw5QkSRpKTQYq4MPAocB3gQf6Xd6dY00aZXY3\nkyRJUrWpySV/KaVaDYIaJpdhSZIkqZoYTFSzDFNS9arlhkeSJO0PA5UkaUQUi0UWLFhCQ8NMpk07\nlYaGmSxYsIRisZh3aZIkjZqaXPInSaouxWKRlpb5dHefS2/vUvrO3dXRsZ4NG+a7z1GSNGY5QyVJ\nGrZFiy4ph6m+EyEDBL29s+nuXsjixSvyLE+SpFFjoJIkDdu6dRvp7Z016HW9vbNZu3ZjhSuSJKky\nDFSSpGFJKdHTM4ldM1MDBT09E6umUUW11CFJGhsMVJKkYYkI6up2suuE2wMl6up25tqZ04YZkqTR\nYqCSJA3bnDkzKBTWD3pdoXAzc+ceX+GKdulrmNHR0cK2bbdy//3fZtu2W+noaKGlZb6hah85sydJ\ngzNQSZKGbfny82lsXEmhcBO7ZqoShcJNNDauYtmy83KrzYYZ2TmzJ0l7Z6CSJA1bfX09mzatoa1t\nM9Onn8zUqfOYPv1k2to2594y3YYZ2TizN3zO6knjg+ehkiSNiPr6etrbl9LeXvogmeeeqT770zCj\nGuqtJrvP7PXpm9lLLF68gvb2pXmVV7WKxSKLFl3CunUb6emZRF3dTubMmcHy5ed7LjZpjHKGSpI0\n4qolnNRCw4z+qmlGw5m9/VdLs3rV9FyTap2BSpI0plVzwwyozn1KtdYKv1pU+369anyuSWOBgUqS\nNKZVc8OMap3RqKWZvWoKddU8q1etzzVpLDBQSZLGtGpumFHNMxrVPLNXjTMt1T6rV83PNanWRTV9\nszOaIqIJ2LJlyxaampryLkeSlJNqakDR0DCTbdtuZfAP4Ynp009m69ZbK10WsGtGo7t7Yb8P4YlC\n4WYaG1flFkZ31XVueTaor671NDauzDUk7/2/51+zdet3Kl0WUN3PNSkPXV1dNDc3AzSnlLqGM5Yz\nVJKkcaVawlS1z2hU68xeNc+0VOusXrU/16RaZ9t0SZJysPs+pcFnDfLep1SNrfBL+5SWDnpdaZ/S\nStrbK1tTn+XLz2fDhvl0d6dBZ/WWLVuTS1218FyTapkzVJIk5aRaZzQGUw0ftqt9pqVaZ/Wgtp5r\nUq1xhkqSpJxU64xGtaqFmZZqnNUDn2vSaHKGSpKknFTzjEa1qqWZlmoJU+BzTRpNdvmTJKlKVNOM\nRrWq1u6DtcbnmsY7u/xJkjQG+QF375xpGRk+16SR4x4qSZJUU6p1n5Kk8ckZKkmSVLMMU5LyZqCS\nJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkVY3xco5UjR0GKkmSJOWqWCyyYMESGhpm\nMm3aqTQ0zGTBgiUUi8W8S5P2yvNQSZIkKTfFYpGWlvl0d59Lb+9SIIBER8d6NmyY7wmbVfWcoZIk\nSVJuFi26pBymZlMKUwBBb+9sursXsnjxijzLk/bKQCVJkqTcrFu3kd7eWYNe19s7m7VrN1a4Imn/\nGKgkSZKUi5QSPT2T2DUzNVDQ0zPRRhWqagYqSZIk5SIiqKvbCQwVmBJ1dTuJGCpwSfmr2UAVESdE\nxNqIuD8ieiNibt41SZIkaf/MmTODQmH9oNcVCjczd+7xFa5I2j81G6iAScBdwEcZ+msNSZIkVbHl\ny8+nsXElhcJN7PpIlygUbqKxcRXLlp2XZ3nSXtVs2/SU0s3AzQDhPLAkSVJNqq+vZ9OmNSxevIK1\na1fS0zORurrHmTt3BsuW2TJd1a9mA5UkSZLGhvr6etrbl9LeXmpU4Xfl+8d/s3zV8pI/SZIkjTHV\nGgyqrdNgsVhkwYIlNDTMZNq0U2lomMmCBUsoFot5lzbujLsZqoULFzJ58uTdjrW2ttLa2ppTRZIk\nSapGxWKRRYsuYd26jfT0TKKubidz5sxg+fLzc12KWCwWaWmZXz4h8lJKbecTHR3r2bBhPps2uVSy\nv87OTjo7O3c7tmPHjhEbP6otbWcREb3AqSmltXu4TROwZcuWLTQ1NVWuOEmSJNWc3UPLLPpCS6Gw\nnsbGlbmGlgULltDR0UJv7+znXFco3ERb22ba25dWvrBBVOtyxK6uLpqbmwGaU0pdwxnLJX+SJEnS\nAIsWXVIOU7PZdeLhoLd3Nt3dC1m8eEVuta1bt7Ec8p6rt3c2a9durHBFuxtvyxFrNlBFxKSIOC4i\nXls+dHT592m5FiZJkqSaV62hJaVET88kdoW8gYKenom57fnqm9nr6Ghh27Zbuf/+b7Nt2610dLTQ\n0jJ/TIaqmg1UwOuBHwFbKJ20YAXQBXwyz6IkSZJU26o5tEQEdXU7Gfo0rIm6up25LbOr5pm90VKz\ngSql9L2UUiGlNGHA5ey8a5MkSVLtqvbQMmfODAqF9YNeVyjczNy5x1e4ol2qdWZvNNVsoJIkSZJG\nSzWHluXLz6excSWFwk3sCn2JQuEmGhtXsWzZebnUVc0ze6PJQCVJkiQNUK2hBUonQt60aQ1tbZuZ\nPv1kpk6dx/TpJ9PWtjnX7oPVPrM3WsbdeagkSZKkvekLLYsXr2Dt2pX09Eykru5x5s6dwbJl+Z/n\nqb6+nvb2pbS3V1dr8jlzZtDRsX6Ilu75zuyNljFxHqp94XmoJEmSlFU1hZZqtuv8XQv7NaZIFAo3\n09i4qmpOOux5qCRJkqQKMkztm2pdjjiaXPInSZIkacRU63LE0eIMlSRJkqRRMdbDFBioJEmSJCkz\nA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBSpIkSZIyMlBJkiRJUkYGKkmSJEnKyEAlSZIkSRkZqCRJ\nkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZ\nGagkSZIkKSMDlSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSZIkScrIQCVJ\nkiRJGRmoJEmSJCkjA5UkSZIkZWSgkiRJkqSMDFSSJEmSlJGBShpHOjs78y5Byp2vA8nXgTSSajpQ\nRcQ5EbE1Iv4SEXdGxBvyrkmqZr6BSr4OJPB1II2kmg1UEXE6sAJYArwO+DGwPiKOyLUwSZIkSeNG\nzQYqYCFweUrpqpTSz4EPA48DZ+dbliRJkqTxoiYDVUTUAc3AbX3HUkoJ+A7QklddkiRJksaXA/Iu\nIKMjgAnAQwOOPwQcO8R9DgLo7u4exbKk6rZjxw66urryLkPKla8DydeB1C8THDTcsaI0sVNbImIK\ncD/QklLa3O/4Z4C3pJSeM0sVEe8FrqlclZIkSZKq3PtSStcOZ4BanaHaDjwDHDng+JHA74e4z3rg\nfcA24IlRq0ySJElStTsImE4pIwxLTc5QAUTEncDmlNLHy78HcB9waUrps7kWJ0mSJGlcqNUZKoCV\nwBURsQX4IaWufxOBK/IsSpIkSdL4UbOBKqW0unzOqQspLfW7C5iVUvpjvpVJkiRJGi9qdsmfJEmS\nJOWtJs9DJUmSJEnVYFwEqog4JyK2RsRfIuLOiHhD3jVJlRIRSyKid8Dl7rzrkkZTRJwQEWsj4v7y\nc37uILe5MCIeiIjHI+LWiHhFHrVKo2Vvr4OI+Pog7w835lWvNNIi4p8i4ocR8aeIeCgivhkRrxzk\ndsN6PxjzgSoiTgdWAEuA1wE/BtaX919J48VPKe01fHH5cny+5UijbhKlvbUfBZ6ztj0i/gFoAz4E\nvBHYSem94XmVLFIaZXt8HZTdxO7vD62VKU2qiBOAzwFvAmYCdcAtEXFw3w1G4v1gzO+hGqK9+m8p\ntVe/ONfipAqIiCXAvJRSU961SHmIiF7g1JTS2n7HHgA+m1JaVf79UOAh4MyU0up8KpVGzxCvg68D\nk1NK78yvMqlyyhMqfwDeklL6QfnYsN8PxvQMVUTUAc3AbX3HUilBfgdoyasuKQfHlJd83BMRV0fE\ntLwLkvISEQ2Uvonv/97wJ2Azvjdo/DmxvBTq5xFxWUS8IO+CpFH0fEqztY/AyL0fjOlABRwBTKCU\nMvt7iNI/njQe3AmcBcwCPgw0AN+PiEl5FiXl6MWU3lB9b9B4dxPwt8BfARcAbwVuLK/mkcaU8vP6\nX4EfpJT69pKPyPtBzZ6HStK+SSmt7/frTyPih8C9wLuBr+dTlSQpbwOWM/0sIn4C3AOcCPxnLkVJ\no+cy4FXAjJEeeKzPUG0HnqG02bK/I4HfV74cKX8ppR3ALwE7mmm8+j0Q+N4g7SaltJXSZyffHzSm\nRMTngf8DnJhSerDfVSPyfjCmA1VKqQfYAryt71h5uu9twB151SXlKSIOofRm+eDebiuNReUPjb9n\n9/eGQyl1gfK9QeNWRLwUOBzfHzSGlMPUPOCklNJ9/a8bqfeD8bDkbyVwRURsAX4ILAQmAlfkWZRU\nKRHxWWAdpWV+U4FPAj1AZ551SaOpvEfwFZS+eQQ4OiKOAx5JKf2W0jr6xRHxa2Ab8Cngd8C3cyhX\nGhV7eh2UL0uANZQ+UL4C+AylFQzrnzuaVHsi4jJKpwKYC+yMiL6ZqB0ppSfKPw/7/WDMt00HiIiP\nUtpseSSl8zF8LKX0X/lWJVVGRHRSOg/D4cAfgR8Ai8rfykhjUkS8ldIekIFvclemlM4u32YppfOO\nPB+4HTgnpfTrStYpjaY9vQ4onZvqW8BrKb0GHqAUpP45pfTHStYpjZby6QIGCzvvTyld1e92SxnG\n+8G4CFSSJEmSNBrG9B4qSZIkSRpNBipJkiRJyshAJUmSJEkZGagkSZIkKSMDlSRJkiRlZKCSJEmS\npIwMVJIkSZKUkYFKkiRJkjIyUEmSJElSRgYqSVKuIuI/I2Jl3nX0FxG9ETE37zokSdUvUkp51yBJ\nGsci4vlAT0ppZ0RsBVallC6t0GMvAU5NKb1uwPEXAY+mlHoqUYckqXYdkHcBkqTxLaX02EiPGRF1\n+xGGnvNlh0haAAAgAElEQVTNYkrpDyNckiRpjHLJnyQpV+Ulf6si4j+BlwGrykvunul3m+Mj4vsR\n8XhE3BsR7RExsd/1WyNicURcGRE7gMvLxy+KiF9ExM6IuCciLoyICeXrzgSWAMf1PV5E/G35ut2W\n/EXEqyPitvLjb4+IyyNiUr/rvx4R34yI8yLigfJtPt/3WJKksctAJUmqBgn4G+B3wP8FXgxMAYiI\nlwM3Af8GvBo4HZgBfG7AGOcBdwGvBT5VPvYn4G+BRmAB8EFgYfm6bwArgJ8BR5Yf7xsDCysHt/XA\nw0Az8C5g5iCPfxJwNHBi+THPKl8kSWOYS/4kSVUhpfRYeVbqzwOW3P0jcHVKqS/A/CYi/h74bkR8\nJKX0VPn4bSmlVQPG/HS/X++LiBWUAtklKaUnIuLPwNMppT/uobT3AQcCf5tSegLojog2YF1E/EO/\n+z4CtKXS5uRfRsR/AG8Dvrq//xaSpNphoJIkVbvjgNdExBn9jkX5zwbgF+Wftwy8Y0ScDnwMeDlw\nCKX3vR37+fj/A/hxOUz12UhplcexQF+g+lnavdPTg5Rm1CRJY5iBSpJU7Q6htCeqnV1Bqs99/X7e\n2f+KiHgzcDWlJYS3UApSrcC5o1TnwCYYCZfWS9KYZ6CSJFWTp4CBjRy6gFellLbu51j/G9iWUrqo\n70BETN+HxxuoGzgzIg5OKf2lfOx44Bl2zY5JksYpvzmTJFWTbcBbIuIlEXF4+dhngP8dEZ+LiOMi\n4hURMS8iBjaFGOhXwFERcXpEHB0RC4BTB3m8hvK4h0fE8wYZ5xrgCeDKiPifEXEScClw1V72XkmS\nxgEDlSQpb/33Hf0zMB24B/gDQErpJ8BbgWOA71OasVoK3D/EGJTvtw5YRakb34+ANwMXDrjZGuBm\n4D/Lj/eegeOVZ6VmAS8AfgisBm6ltDdLkjTOxe77ZyVJkiRJ+8oZKkmSJEnKyEAlSZIkSRkZqCRJ\nkiQpIwOVJEmSJGVkoJIkSZKkjAxUkiRJkpSRgUqSJEmSMjJQSZIkSVJGBipJkiRJyshAJUmSJEkZ\nGagkSZIkKSMDlSRJkiRlVDWBKiLOiYitEfGXiLgzIt6wl9u/LyLuioidEfFARHw1Il5QqXolSZIk\nqSoCVUScDqwAlgCvA34MrI+II4a4/QzgSuDLwKuAdwFvBL5UkYIlSZIkCYiUUt41EBF3AptTSh8v\n/x7Ab4FLU0oXD3L784APp5SO6XesDbggpXRUhcqWJEmSNM7lPkMVEXVAM3Bb37FUSnnfAVqGuNsm\nYFpEvL08xpHAacB/jG61kiRJkrRL7oEKOAKYADw04PhDwIsHu0NK6Q7gDOAbEfEU8CDwKNA2inVK\nkiRJ0m4OyLuALCLiVUA7sBS4BZgCXAJcDnxwiPscDswCtgFPVKJOSZIkSVXpIGA6sD6l9PBwBqqG\nQLUdeAY4csDxI4HfD3GffwQ2ppRWln//aUR8FLg9IhallAbOdkEpTF0zEgVLkiRJGhPeB1w7nAFy\nD1QppZ6I2AK8DVgLzzaleBtw6RB3mwg8NeBYL5CAGOI+2wCuvvpqGhsbh1m1tGcLFy5k1apVeZeh\nccDnmirF55oqxeeaKqG7u5szzjgDyhlhOHIPVGUrgSvKweqHwEJKoekKgIj4F+AlKaUzy7dfB3wp\nIj4MrAdeAqyi1ClwqFmtJwAaGxtpamoarb+HBMDkyZN9nqkifK6pUnyuqVJ8rqnChr0VqCoCVUpp\ndfmcUxdSWup3FzArpfTH8k1eDEzrd/srI+IQ4BxKe6ceo9Ql8B8rWrgkSZKkca0qAhVASuky4LIh\nrnv/IMc6gI7RrkuSJEmShlINbdMlSZIkqSYZqKRR0NramncJGid8rqlSfK6pUnyuqdZESinvGioi\nIpqALVu2bBlyo+N9993H9u3bK1uYBnXEEUdw1FFH5V2GJEmSxqCuri6am5sBmlNKXcMZq2r2UOXt\nvvvuo7GxkccffzzvUgRMnDiR7u5uQ5UkSZKqmoGqbPv27Tz++OOep6oK9J0XYPv27QYqSZIkVTUD\n1QCep0qSJEnSvrIphSRJkiRlZKCSJEmSpIwMVJIkSZKUkYFKkiRJkjIyUGnYpk+fztlnn513GZIk\nSVLFGajGiU2bNvHJT36SP/3pTyM+dqFQICJGfFxJkiSp2tk2fZy44447uPDCC3n/+9/PoYceOqJj\n/+IXv6BQMJtLkiRp/PFT8DCklGpm7H0dL6XEk08+uV9j19XVMWHChCxlSZIkSTXNQLWfisUiCxYs\noaFhJtOmnUpDw0wWLFhCsVis2rE/+clPcsEFFwCl/U6FQoEJEyZw7733UigUWLBgAddeey2vfvWr\nOeigg1i/fj0Al1xyCTNmzOCII45g4sSJvP71r2fNmjXPGX/gHqorr7ySQqHAHXfcwbnnnsuLXvQi\nDjnkEN75znfy8MMPD+vvIkmSpN2N5pf82juX/O2HYrFIS8t8urvPpbd3KRBAoqNjPRs2zGfTpjXU\n19dX3djz58/nl7/8Jddddx3t7e0cfvjhRAQvfOELAbjttttYvXo1bW1tHHHEEUyfPh2ASy+9lHnz\n5nHGGWfw1FNPcd111/Hud7+bG264gbe//e3Pjj/U/qmPfexjvOAFL2Dp0qVs27aNVatW0dbWRmdn\nZ6a/hyRJkkqKxSKLFl3CunUb6emZRF3dTubMmcHy5edn/syobAxU+2HRokvKgWd2v6NBb+9sursT\nixevoL19adWN/epXv5qmpiauu+465s2bx1FHHbXb9b/85S/56U9/yrHHHrvb8V/96lcceOCBz/7e\n1tbG6173OlauXLlboBrKC1/4Qm6++eZnf3/mmWf43Oc+R7FY9IUuSZKU0Wh+Ea/955K//bBu3UZ6\ne2cNel1v72yuv34jXV1kulx//Z7HXrt246j9vU488cTnhClgtzD12GOP8eijj3LCCSfQ1dW11zEj\ngg996EO7HTvhhBN45plnuPfee4dftCRJ0ji1+xfxfSuF+r6IX8jixSvyLG/ccYZqH6WU6OmZxK4n\n7UDBAw9MpLk57eE2Q44O7Hnsnp6JpJRGpT153xK/gW644QaWL1/OXXfdtVujin3t6Ddt2rTdfj/s\nsMMAePTRR7MVKkmSpPKX/EsHva63dzbXXLOSt70Npk4tXV74QrB/2OgxUO2jiKCubiel8DNYqElM\nmbKTG27IEniCU07ZyYMPDj12Xd3OUTvX08EHH/ycY7fffjvz5s3jxBNP5Atf+AJTpkyhrq6Or33t\na/u8B2qozn9unJQkScqmtzfx5z/v+Yv4hx+eyLx5uz5XTpgAU6bAS15SClh9f/b/+SUvgUMPBU8t\nuv8MVPthzpwZdHSsH7DPqaRQuJnTTjuepqZsY7/rXXsee+7c47MNXLa/Yezf//3fOfjgg1m/fj0H\nHLDrafLVr351WHVIkiRp//X2wg03wGc+E2zfvucv+adP38mmTcEDD8D99/Psn30/f//7pT8HNl+e\nNOm5IWtg8JoyBZ73vAr8hWuIgWo/LF9+Phs2zKe7O/Vbs5ooFG6msXEVy5Y9t6V4NYwNMGnSJKC0\nF2pgU4rBTJgwgYjg6aeffjZQbdu2jW9/+9vDqkOSJEn7rqcHOjvhM5+Bu++G44+HU06ZwY037vmL\n+Be/GF78Yvb4Zf8TT5SC1WDB67e/hTvvLP38xBO73++FLxw8ePUPYIcfDvu4S6TmGaj2Q319PZs2\nrWHx4hWsXbuSnp6J1NU9zty5M1i2bHjdVEZzbIDm5mZSSnziE5/gPe95D3V1dcyZM2fI27/jHe9g\n5cqVzJo1i/e+97089NBDXHbZZRxzzDH893//914fb6hlfS73kyRJ2rudO+ErX4EVK0rh5pRT4PLL\nS4GqWDy/3OVveF/EH3QQHH106TKUlOCxx3YPXP2D149+VJo5e+ih0ixan7q6UsAaanlh37Hyd/41\nzUC1n+rr62lvX0p7OyPeJGI0x37961/PsmXL+OIXv8j69etJKXHPPfcQEYM+zkknncTXvvY1Lrro\nIhYuXEhDQwMXX3wxW7dufU6gGmyMoWofrX1gkiRJY8HDD8PnPw+XXgo7dsB73wsXXACvfvWu24z2\nF/H9RcBhh5Uu/WsY6OmnS6FqqOB1992lP3fs2P1+hx66531dU6eWZtoOGKHU0nf+ruuvv2lkBgRi\nvMwYREQTsGXLli00DTL32dXVRXNzM0Ndr8rxv4UkSRpv7rsPVq6EL3+5NCv0wQ/CeefBy1629/uO\nVifo0bBz5+7LDAcLYA88AE89tes+EXDkkXvf33XYYXtuqrH7+bteCLweoDmltPdzAu2BM1SSJElS\nTn72M7j4Yrj2Wqivh/PPh7a20j6lfVUrYQpKS/yOOaZ0GUpKpZm6wRpq3H8/bN5c+vMPf9j9fgcd\ntOd9XV/5Sv/zdw0rQ+3GQCVJkiRV2B13wEUXwbp18NKXwmc/W5qVOuSQvCvLXwQccUTpctxxQ9/u\nqafg978fOnjddVfpzz//ue8eG4GlI16vgUqSJEmqgJTgxhtLHftuvx0aG+HrXy/tk7IV+f573vPg\nqKNKlz3505/g/vsTb3nLJLZvH/nZvHHSzFCSJEnKx9NPwzXXlGZbTjml1Ar9W9+Cn/4UzjrLMDXa\nDj0UGhuDQw7pO3/XyKqaQBUR50TE1oj4S0TcGRFv2MNtvx4RvRHxTPnPvstPKlmzJEmSNJTHHy91\n7DvmGDjjjNLSvu99r7Tcb9688XOepmoxZ84MCoX1Iz5uVfxnjIjTgRXAEuB1wI+B9RFxxBB3WQC8\nGJhS/vOlwCPA6tGvVpIkSRrao4/CsmWlDn0f/zi0tJT289x4I7zlLXvuRKfRs3z5+TQ2rqRQuImR\nnKmqikAFLAQuTyldlVL6OfBh4HHg7MFunFIqppT+0HcB3gg8H7iiUgVLkiRJ/f3ud6VW50cdBcuX\nw7vfDb/6VamD356aK6gy+s7f1da2mSlTPjpi4+YeqCKiDmgGbus7lkonx/oO0LKPw5wNfCel9NuR\nr1CSJEka2s9/Dh/4ABx9NHzta6VZqXvvhY6O0jFVj/r6etrbl3LDDV8YsTGrocvfEcAE4KEBxx8C\njt3bnSNiCvB24D0jX5okSZI0uM2bSx37vvUtmDIFPv1p+NCHSk0QNH5UQ6AarrOAR4Fv51yHJEmS\nxriU4JZbSueQ+u534ZWvhC9/udR04sAD865OeaiGQLUdeAY4csDxI4Hf78P93w9clVJ6el8ebOHC\nhUyePHm3Y62trRx77F4nwyRJkjROPf00XH99aUbqrrvgDW+ANWtK3fomTMi7Ou1JZ2cnnZ2dux3b\nsWPHiI2fe6BKKfVExBbgbcBagIiI8u+X7um+EXEi8HLgq/v6eKtWraKpqek5x7u6uva9aEmSJI0L\nf/kLXHklfPaz8JvfwMknw223wUkn2a2vVrS2ttLa2rrbsa6uLpqbm0dk/NybUpStBP4uIv42Iv4H\n8EVgIuWufRHxLxFx5SD3+wCwOaXUXbFKxRVXXEGhUOC+++7LuxRJkqRR8dhj8C//AtOnwznnwOtf\nD1u2wPr18Fd/ZZjSLrnPUAGklFaXzzl1IaWlfncBs1JKfyzf5MXAtP73iYhDgb+hdE4qVVBEEP5f\nRJIkjUEPPAD/+q/wxS/Ck0/C+98P558Pr3hF3pWpWlVFoAJIKV0GXDbEde8f5NifgENGu649SSmN\nWrAYzbElSZK0u1/9qrSs78or4aCD4KMfLbU/nzIl78pU7aplyV/NKBaLLLhgAQ1NDUx74zQamhpY\ncMECisViVY8tSZKk5/qv/4LTToNjj4V16+DCC+G++0pd/AxT2hcGqv1QLBZpObmFjgc72DZ3G/ef\ncj/b5m6j4/cdtJzcMqzgM5pjr1mzhkKhwO233/6c6y6//HIKhQJ33303P/nJTzjrrLN4+ctfzsEH\nH8yUKVP4wAc+wCOPPJL5sSVJkqpNSvCd78Bf/3WpW9+PflRa4rd1K/zDP8CAhtDSHhmo9sOiTy2i\n+xXd9L6iF/pW4wX0vryX7ld0s3jZ4qoc+x3veAeHHHIIq1evfs51q1ev5jWveQ2vetWruPXWW9m2\nbRtnn302n//852ltbeW6667jHe94R+bHliRJqhbPPFNqff6GN5TC1COPwDe+Ab/4RemEvAcdlHeF\nqkVVs4eqFqz7zjp65/YOel3vy3u5/lvXc+bfn5lp7OvXX0/v3ww99tp1a2mnPdPYBx10EHPmzOH6\n66/n0ksvfXZv1kMPPcT3vvc9LrzwQgDOOecczj333N3u+6Y3vYn3vve9bNy4kRkzZmR6fEmSpDw9\n+SRcdVVpj9SvflXq0nfLLTBzpt36NHwGqn2UUqJnQs+u2aOBAh544gGaL28e+jZDDg48yR7H7in0\nDKtRxemnn851113Hd7/7XU466SQA/u3f/o2UEu9+97sBOLDf6b2ffPJJ/vznP/OmN72JlBJdXV0G\nKkmSVFP+9Ce4/HJYtQp+/3t45zvhmmtKM1TSSDFQ7aOIoO6ZulL4GSzTJJhy4BRu+H9vyDT+Kd88\nhQfTg0OOXfdM3bC6/s2ePZtDDz2Ub3zjG88GqtWrV/Pa176WV5T7gD766KMsXbqUb3zjG/zhD394\n9r4RMaJnk5YkSRpNDz0E7e1w2WXw+ONw5pml1ufHHpt3ZRqLDFT7Yc7MOXT8poPelz93aV7hngKn\nzT6NpilNmcZ+16x37XHsuX89N9O4fZ73vOdx6qmn8s1vfpPLLruMBx98kI0bN3LRRRc9e5vTTjuN\nO++8kwsuuIDjjjuOQw45hN7eXmbNmkVv7+DLESVJkqrFPffAJZfA178OdXXw4Q/D3/89TJ2ad2Ua\nywxU+2H5/13OhpM30J26S8EngFQKPI2/bmTZZcuqcuw+p59+OldddRW33XYbP/vZzwCeXe732GOP\nsWHDBj71qU+xaNGiZ+/z61//etiPK0mSNJruugs+8xlYvRoOPxz++Z/hIx+Bww7LuzKNB3b52w/1\n9fVsumUTbS9pY/q66Uy9YSrT102n7SVtbLplE/X19VU5dp+ZM2dy2GGHcd1117F69Wre+MY38rKX\nvQyACRMmADxnJmrVqlWeYFiSJFWdlOC734XZs+F1r4PNm+Hzn4d774VPfMIwpcpxhmo/1dfX0/6Z\ndtppH1aTiEqPDXDAAQfwzne+k+uuu47HH3+cFStW7PbYb3nLW7j44ot56qmnmDp1Krfccgvbtm0j\npTSidUiSJGXV2wvf/nbpxLs//CH8r/8F115bOjnvAX6yVQ6coRqG0Zy5Ga2xTz/9dHbu3ElEcNpp\np+12XWdnJ7NmzeKyyy7jE5/4BAceeCA33XQTEeEslSRJqoihvsh96qnS3qhXvarUre/gg+Gmm0rL\n/VpbDVPKj0+9ceZtb3sbzzzzzKDXTZkyheuvv/45xwfe/swzz+TMM7Odb0uSJGmgYrHIokWXsG7d\nRnp6JlFXt5M5c2awfPn5QD1f/jKsXAn33w+nngpXXAFvfnPeVUslBipJkiTlplgs0tIyn+7uc+nt\nXUpfZ66OjvV0ds6np2cNO3fWc8YZcMEF0NiYc8HSAAYqSZIk5WbRokvKYWp2v6NBb+9stm9PvPa1\nK1i7dinTpuVWorRH7qGSJElSbtat20hv76whrp3NY49tNEypqhmoJEmSlIve3sSf/zyJ0jK/wQQ9\nPRPtOKyqZqCSJElSRfX2wtq1cMIJwfbtO4GhAlOirm6n3YZV1QxUkiRJqoieHrjqKnjNa2DePCgU\nYM6cGRQK6we9faFwM3PnHl/hKqX9Y6CSJEnSqNq5E9rb4eUvhzPPhKOPhttvL12uueZ8GhtXUijc\nxK6ZqkShcBONjatYtuy8PEuX9spAJUmSpFHx8MPwyU/CUUfBeefBiSfCT34C69bB8eWJp/r6ejZt\nWkNb22amTz+ZqVPnMX36ybS1bWbTpjXU19fn+neQ9sa26QN0d3fnXcK4538DSZJq2333lU7E++Uv\nQ0rwd38H554LL3vZ4Levr6+nvX0p7e2QUnLPlGqKgarsiCOOYOLEiZxxxhl5lyJg4sSJHHHEEXmX\nIUmS9sPPfgYXXwzXXgv19XD++fCxj8H+vKUbplRrDFRlRx11FN3d3Wzfvj3vUkQp4B511FF5lyFJ\nkvbBHXfAZz5T6tz30pfCZz8LH/wgHHJI3pVJo89A1c9RRx3lh3hJkqR9kBLceGMpSN1+OzQ2wte/\nDu99LzzveXlXJ1WOTSkkSZK0z55+Gq65Bo47Dk45pdQK/Vvfgp/+FM46yzCl8cdAJUmSpL16/HH4\n/OfhmGPgjDNKS/u+973Scr++c0pJ45FL/iRJkjSkRx+Fjo7SeaQeeQROP700I3XccXlXJlUHA5Uk\nSZKe43e/g1Wr4EtfKi3zO/vs0rmkjj4678qk6mKgkiRJ0rN+/vNS6/Orr4ZJk+DjH4cFC+BFL8q7\nMqk6GagkSZLE5s2ljn3f+hZMmQKf/jR86ENw6KF5VyZVt6rZPhgR50TE1oj4S0TcGRFv2MvtnxcR\nyyNiW0Q8ERG/iYizKlSuJElSzUsJ1q+Hk06CN7+5dGLeL38ZfvOb0kl5DVPS3lXFDFVEnA6sAD4E\n/BD+f/buPDzK6u7/+PtMGCAJQ9hXWQKIhKrFRLSUulQRqC1xwVbRVqv92boVH6z1aQuttkK1Klis\nUO3yiHahLrQaXAARl4ookIh1CVR2hIBAIAxZyCRzfn+cyTLJBJLJMpPk87quXJm55557vgOB5JNz\nzvcwA1hujBlpra1rp91ngd7A9cAWoD9xFBBFRERE4lVZGTz3nBuR2rABzjwTlixx3foSEmJdnUjr\nEheBChegHrfWPgVgjLkJ+DpwA/BAzZONMZOBc4Bh1trDocM7W6hWERERkVapuBiefBIefNCNQl10\nEbz2mhuhMibW1Ym0TjEf0THGeIEM4LWKY9ZaC6wExtXxtCnAeuB/jTGfGWM2GWMeNMZ0bvaCRURE\nRFqZw4fhvvtg6FC49VY3IpWdDStWwAUXKEyJNEY8jFD1AhKAfTWO7wNOqeM5w3AjVCXApaFr/B7o\nAXyvecoUERERaV327IHf/hYeewyOHYPrr3dro0aMiHVlIm1HPASqaHiAIHC1tfYogDHmDuBZY8wt\n1tpjdT1xxowZpKSkhB2bNm0a06ZNa856RURERFrMp5+6aX1PPgmdO8Mtt7j25/37x7oykZa3ePFi\nFi9eHHasoKCgya5v3Oy62AlN+SsCplprs6odXwSkWGsvi/CcRcCXrbUjqx0bBXwMjLTWbonwnHQg\nOzs7m/T09CZ/HyIiIiKxtn69azSxZInbN2rGDLjpJqjxu2SRdi8nJ4eMjAyADGttTmOuFfM1VNba\nAJANXFhxzBhjQvffqeNpq4EBxpikasdOwY1afdZMpYqIiIjEHWth5UqYMAHGjoX334ff/x62b4f/\n/V+FKZHmFvNAFTIPuNEYc21opOkxIAlYBGCMuc8Y82S18/8OHASeMMakGWPOxXUD/PPxpvuJiIiI\ntBXl5a71+dixrltffj48/TRs2gQ/+IGb6icizS8u1lBZa58xxvQCfgX0BTYAk6y1+0On9AMGVTu/\n0BhzEfA7YB0uXD0N/LxFCxcRERFpYceOwVNPuTVSn37quvStWOFGqNStT6TlxUWgArDWLgQW1vHY\n9RGO/ReY1Nx1iYiIiMSDI0dct77f/hb27oXLLoO//hXOOivWlYm0b3ETqERERESktn37YP58WLgQ\niorg2mvhxz+GU+raXEZEWpQClYiIiEgc2rIFHnoInngCvF7Xre9//gcGDox1ZSJSnQKViIiISBx5\n/33X+vzZZ6FnT/j5z90+Ut27x7oyEYlEgUpEREQkxqyFN95wQWr5chg6FH73O7j+ekhMjHV1InI8\n8dI2XURERKTdCQbhX/+CL33JdevLy4O//91177vlFoUpkdZAgUpERESkGVhr63ystBT+7/9g9Gi4\n/HIXnF5+GTZsgGnToIPmEIm0GgpUIiIiIk3E7/czffrdpKZOYNCgS0lNncD06Xfj9/tDj8PcuTBs\nGHzvezBqFLzzjpvu97WvaR8pkdZIv/8QERERaQJ+v59x46aSm3sHweA9gAEsCxYsZ8WKqWRmLuGP\nf/Rx9Ch8+9uu9fno0TEuWkQaTYFKREREpAnMnPlQKExNrnbUEAxOZtMmy8MPz+W22+7hjjtg0KCY\nlSkiTUxT/kRERESawNKlqwkGJ9Xx6GQGDFjNww8rTIm0NQpUIiIiIo1kraW0NBk3zS8SQ3l50nEb\nVYhI66QpfyIiIiKNsH07PPaYYe/eQsASOVRZvN5CjLpOiLQ5GqESERERaaBgEF55BaZMcR37HnsM\nTjttPB7P8ojnezzLyMz8SgtXKSItQYFKREREpJ4OHoSHHoKTT4aLL4Zdu+Dxx2H3bvj3v+8kLW0e\nHs8ruJEqAIvH8wppaQ8ze/aPYlm6iDQTTfkTEREROYF162DhQvjHP9zo1De/CX/9K3zpS9X3jvKx\nZs0SZs2aS1bWPAKBJLzeIjIzxzN79hJ8Pl8s34KINBMFKhEREZEIiovh6addkFq3DgYPhl/8wm3I\n26dP5Of4fD7mz7+H+fNdowqtmRJp+xSoRERERKrZssWtifq//4P8fJg0CbKy3BS/hIT6X0dhSqR9\nUKASERGRdq+83DWZWLgQli2Dbt3g+uvh5pthxIhYVyci8UyBSkRERNqt/fvdSNRjj7n25xkZ8Oc/\nw5VXQlJSrKsTkdZAgUpERETaFWvhvffcaNTTT7umElde6W6PHVu9yYSIyIkpUImIiEi7UFQEixe7\nIJWTA6mpMHu2m9rXq1esqxOR1kqBSkRERNq0Tz+F3/8enngCCgpcc4mXXnLNJhrSZEJEJBIFKhER\nEWlzysvhxRfdaNSKFdCzJ3z/+/CDH8CwYbGuTkTaEgUqERERaTP27XNNJR5/HHbuhLPPhiefhG99\nC1SlWPkAACAASURBVDp3jnV1ItIWKVCJiIhIq2YtvPOOG4169lk3je/qq+GWW1zXPhGR5qRAJSIi\nIq3S0aPw97+7IPXBB26/qPvvh+9+F3r0iHV1ItJeRBWojDFftda+3tTFiIiIiJzIxo2uycSiRS5U\nfeMb8MADMGECeDyxrk5E2ptoR6iWGWM+A54AnrTW7mrCmkRERETClJVBVhYsWACrVkHv3nDrra7J\nxJAhsa5ORNqzaH+PMxB4FLgC2GqMWW6M+ZYxpmPTlSYiIiLtXV4e3HsvDB0KU6dCSQn87W+waxf8\n+tcKUyISe1EFKmvtAWvtw9baMcDZwH+BhcAeY8wjxpgvNvSaxphbjTHbjDHFxph3jTFjj3PuecaY\nYI2PcmNMn2jej4iIiMQPa+Gtt+DKK2HwYLcu6utfh/ffh9WrXcOJTp1iXaWIiNPomcbW2hzgPtyI\nVRfgBiDbGPNvY8wX6nMNY8yVwFzgbuAM4ANguTHmePuWW+BkoF/oo7+19vOo34iIiIjElN/v1kad\nfjqcd55rNDF3Luze7dqgjxkT6wpFRGqLOlAZY7zGmCuMMS8DO4BJwG1AX2BE6Niz9bzcDOBxa+1T\n1tqNwE1AES6cHc9+a+3nFR9RvRERERGJqY8/duuhBgyAH/4QRo6ElSshNxemT4du3WJdoYhI3aLt\n8vc7YBpggL8Ad1lrP6p2SqEx5k5gTz2u5QUygF9XHLPWWmPMSmDc8Z4KbDDGdAY+Au6x1r7T4Dcj\nIiIiLS4QgOefd00m3nwT+vaFGTPg+9+Hk06KdXUiIvUXbZe/0cAPgX9aa4/Vcc4B4Kv1uFYvIAHY\nV+P4PuCUOp6TB/wAWA90Am4E3jDGnGWt3VCP1xQREZEY2L0b/vAH+OMfXcOJc8+Ff/wDLrsMOqq1\nlYi0QlEFKmvthfU4pwx4M5rr1+Pa/8U1wqjwrjFmOG7q4HXHe+6MGTNISUkJOzZt2jSmTZvW5HWK\niIiIazLx+utuA97nn4fERPjOd+Dmm+G002JdnYi0dYsXL2bx4sVhxwoKCprs+sZa2/AnGfNTYK+1\n9okax28Aeltrf9OAa3lx66WmWmuzqh1fBKRYay+r53UeAMZba8fX8Xg6kJ2dnU16enp9yxMREZEo\nFRTAU0+5ILVxI4weDbfc4sJU166xrk5E2rOcnBwyMjIAMkJN9qIWbVOKHwCfRDj+Ma6hRL1ZawNA\nNlA56mWMMaH7DVkTNQY3FVBERERi6D//gZtugoED4Y473CjU66/DRx+55hMKUyLSlkS7hqofEKmr\n3n6gfxTXmwcsMsZkA2txU/eSgEUAxpj7gAHW2utC928HtuECXGfcGqqvAhdF8doiIiLSSKWlsGSJ\nG416+23Xse/HP4Ybb3S3RUTaqmgD1S5gPC7UVDeeenT2q8la+0xoz6lf4dqubwAmWWv3h07pBwyq\n9pSOuH2rBuCmC/4HuNBa+1ZDX1tEREROzFqLm0ASbtcut0fUH/8In38OX/0qPPssXHIJeL0xKFRE\npIVFG6j+CPw2tP5pVejYhcADuKDTYNbahcDCOh67vsb9B4EHo3kdERERqR+/38/MmQ+xdOlqAoFk\nvN5CpkwZz7333sm6dT4WLICsLEhOhuuuc00mRo+OddUiIi0r2kD1INATF4AqmpyWAL+x1t7XFIWJ\niIhI7Pj9fsaNm0pu7h0Eg/fgtn+0PProch5/fCqlpUs47TQfCxfCNddAly4xLlhEJEaibZtugf81\nxtwLpAHFwKfH2ZNKREREWpGZMx8KhanJ1Y4arJ1Maanliivm8swz9xBhFqCISLsS7QgVANbao8C6\nJqpFREREYsha2LYN1q6FJ59cHRqZimQy69fPU5gSEaERgcoYcybwLWAwVdP+ALDWXt7IukRERKSZ\n5eXBunVVH+vXw8GDAJaEhGTcNL9IDIFAUp2NKkRE2pOoApUx5irgKWA5MBFYAYzEdej7V5NVJyIi\nIk3i8GEXmKoHqM8+c4/16QNnnQXTp8PYsXDmmYazzipk+3ZL5FBl8XoLFaZERIh+hOpnwAxr7QJj\njB+o2BfqcbS5roiISEwVF8P774eHp//+1z3WtSuceaZrJDF2rPsYNIha0/emTBnPggXLa6yhcjye\nZWRmfqUF3omISPyLNlANB14K3S4Fkq211hjzMK6N+t1NUZyIiIgcX1kZfPRReHj68EMoL4dOneCM\nM2DSJJg1y4WnkSPB4znxdefMuZNVq6aSm2tDocp1+fN4lpGW9jCzZy9p7rcmItIqRBuoDgG+0O3d\nwKnAh0A3IKkJ6hIREZEagkHYvDk8PL3/vhuR8njgC19wU/duusmFp1NPhY4dT3zdSHw+H2vWLGHW\nrLlkZc0jEEjC6y0iM3M8s2cvwefznfgiIiLtQLSB6i3gIlyIehaYb4y5IHTstSaqTUREpN2yFnbv\nrt004vBh9/jw4S48XXGFC09nnOE22G1KPp+P+fPvYf581IBCRKQO0Qaq24DOodtzgADwZWAJMLsJ\n6hIREWlX8vPDw9PatbB3r3usf38Xnu68s6JpBPTo0bL1KUyJiETW4EBljOkAfAPX4Q9rbRC4v4nr\nEhERabMKCyEnJzw8bd3qHuvWzYWmG26oahoxcGBs6xURkbo1OFBZa8uMMY8Bac1Qj4iISJtSWuqa\nRFQPT5984tZDJSZCejpkZlaFpxEjanfcExGR+BXtlL+1wBhgRxPWIiIi0qoFg7BpU3h4+uADOHYM\nEhLg9NPhy1+G22934ekLX4AO0X4nFhGRuBDtf+MLgXnGmEFANlBY/UFr7X8aW5iIiEg8sxZ27gwP\nT9nZ4Pe7x085xYWmiv2exoxxI1IiItK2RBuo/hH6/Ei1YxXbqVsgoTFFiYiINJdou9Xt3x8entat\nc8cATjrJNY342c9ceMrIcGuhRESk7Ys2UKU2aRUiIiLNyO/3M3PmQyxduppAIBmvt5ApU8YzZ86d\nEfdT8vvdaFP18LQjNMm9R4/wvZ7GjoV+/Vr4DYmISNyIKlBZa7V2SkREWgW/38+4cVPJzb2DYPAe\nKiZTLFiwnFWrpvLGG0vYutUXFp42bnRT+pKT3WhTxV5PY8dCaqqaRoiISJWoApUx5trjPW6tfSq6\nckRERJrWzJkPhcLU5GpHDcHgZD7+2NKnz1ysvQevF774RTj/fPjxj114SktzzSRERETqEu2Uv/k1\n7nuBJKAUKAIUqEREJC4sXbo6NDIVyWS6d5/HK6+4MNWpU0tWJiIibUG0U/661zxmjDkZ+D3wYGOL\nEhERaQrWWgKBZNw0v0gMiYlJjB0bXaMKERERT1NdyFr7KfATao9eiYiIxIihuLgQ14A2EovXW6gw\nJSIiUWuyQBVSBgxo4muKiIg0mN8PV10F+fnjMWZ5xHM8nmVkZn6lhSsTEZG2JNqmFJk1DwH9gduA\n1Y0tSkREpDE2boTLL4ddu+Cpp+7kN7+ZSm6uDTWmcF3+PJ5lpKU9zOzZS2JdroiItGLRNqV4vsZ9\nC+wHVgE/alRFIiIijfDcc3D99TBokGuBPmqUj0svXcKsWXPJyppHIJCE11tEZuZ4Zs9eEnEfKhER\nkfqKtilFU08VFBERaZSyMvjJT2DuXPjWt+DPf4YuXdxjPp+P+fPvYf5816hCa6ZERKSpRDtCJSIi\nEjf27nXrpd5+Gx5+GG6/ve7NdxWmRESkKUW7hmoJ8K619sEax+8Cxlprv9kUxYmIiJzI6tXwzW+C\ntfD663DOObGuSERE2pNop+6dC7wc4fgrocdERESalbXwyCNw/vkwfDjk5ChMiYhIy4s2UHXBtUiv\nKQB0jb4cERGREysshGuucVP7fvhDWLUK+vePdVUiItIeRRuoPgSujHD8KuCTaC5ojLnVGLPNGFNs\njHnXGDO2ns8bb4wJGGNyonldERFpXf77Xzj7bMjKgqefhnnzwOuNdVUiItJeRduU4l7gn8aY4bhW\n6QAXAtOABq+fMsZcCcwFvg+sBWYAy40xI621B47zvBTgSWAl0LehrysiIq3Lv/4F110HAwbA2rUw\nenSsKxIRkfYuqhEqa+1S4FJgBLAQF4ZOAiZYa2vuUVUfM4DHrbVPWWs3AjcBRcANJ3jeY8DfgHej\neE0REWklKlqiX345TJyoMCUiIvEj6rbp1tqXgJcaW4AxxgtkAL+udm1rjFkJjDvO864HUoFrgJ83\ntg4REYlPn3/uWqK/9RY8+CD86Ed1t0QXERFpadG2TR8LeKy179U4fjZQbq1d34DL9QISgH01ju8D\nTqnj9U/GBbCvWGuD2lNERKRtevdduOIKCARg5UrX0U9ERCSeRNuUYgEwIMLxgaHHmo0xxoOb5ne3\ntXZLxeHmfE0REWlZ1sKCBXDuuTBkiGuJrjAlIiLxKNopf6OBDRGOvx96rCEOAOXUbirRF9gb4Xwf\ncCYwxhhTEd48gDHGlAITrbVv1PViM2bMICUlJezYtGnTmDZtWgPLFhGR5lBUBD/4Afz1rzB9upvm\n17FjrKsSEZHWavHixSxevDjsWEFBQZNd31hrG/4kYw4C37DWrqlx/MvAS9ba7g283rvAe9ba20P3\nDbATeMRa+2CNcw2QVuMStwJfBaYC2621xRFeIx3Izs7OJj09vSHliYhIC9m82TWe2LIF/vQn0O+6\nRESkOeTk5JCRkQGQYa1t1PZL0Y5QrQDuM8ZcYq0tADDGdMOta3o1iuvNAxYZY7KpapueBCwKXfs+\nYIC19jrrEmDYXlfGmM+BEmttbpTvR0REYiwrC669Fvr0gffeg1NPjXVFIiIiJxZtoLoTeAvYYYx5\nP3RsDK6RxHcaejFr7TPGmF7Ar3BT/TYAk6y1+0On9AMGRVmriIjEsfJy+MUv4Ne/hksvhUWLoMbM\nbBERkbgVVaCy1u42xpyOa1n+RaAYeAJYbK0NRHnNhbg9rSI9dv0JnvtL4JfRvK6IiMTOgQNuWt+q\nVXD//XDXXWqJLiIirUtj9qEqNMa8jVvrVLFc+GvGGKy1WU1SnYiItFlr17qW6CUl8OqrcMEFsa5I\nRESk4aLdh2oY8C/gNMDi2pZX726R0PjSRESkLbIWHn8cbr8dzjgDnnsOTjop1lWJiIhEJ9p9qOYD\n24A+QBFwKnAesB44v0kqExGRNqe4GK6/Hm6+Gf7f/4M331SYEhGR1i3aKX/jgAustQeMMUGg3Fr7\ntjHmp8AjwBlNVqGIiLQJW7fC1KmwaRM89RR8p8EtjEREROJPtCNUCYA/dPsAMCB0ewdwSmOLEhGR\ntuWllyAjA/x+ePddhSkREWk7og1UH+G6+wG8B9xljBkP/ALY2hSFiYhI61fREv0b34BzzoH16+H0\n02NdlYiISNOJdsrfbCA5dPsXwIvAv4GDwJVNUJeIiLRyBw/CNde4Dn5z5sBPfgKeaH+NJyIiEqei\n3YdqebXbm4FRxpgewCFrra37mSIi0h6sX+9aoh89CsuWwUUXxboiERGR5tFkvyu01uYrTImIyJ/+\nBOPHQ58+kJOjMCUiIm2bJl+IiEiTKC6G730PbrzRtUb/979h8OBYVyUiItK8ol1DJSIiUmn7dtcS\n/ZNP4Ikn4LvfjXVFIiIiLUOBSkREGmXZMtd8IiUF1qyBMWNiXZGIiEjL0ZQ/ERGJSjAIv/wlXHwx\nfOlLkJ2tMCUiIu2PRqhERKTB8vPd5ryvvOJC1cyZaokuIiLtkwKViIg0yPvvw+WXw5Ej8PLLMHly\nrCsSERGJHf0+UURE6u2JJ+DLX4aePd0UP4UpERFp7xSoRETkhEpK4Ac/gBtugG9/G95+G4YOjXVV\nIiIisacpfyIiclw7dsAVV8CHH7pNe7/3vVhXJCIiEj8UqEREpE4rVsDVV0OXLrB6NWRkxLoiERGR\n+KIpfyIiUkswCHPmuDVSY8e69VIKUyIiIrUpUImISJjDh+HSS2HWLPjFL+DFF10TChEREalNU/5E\nRKTSBx+4luj5+fDSS27TXhEREambRqhERASAp56CL30JunZ1U/wUpkRERE5MgUpEpJ07dgxuuQWu\nuw6uugreeQeGDYt1VSIiIq2DpvyJiLRju3a5lugbNsDjj8ONN4Ixsa5KRESk9VCgEhFpp157zY1I\nJSa6jXrHjo11RSIiIq2PpvyJiLQz1sL998PEiXDGGZCTozAlIiISLQUqEZF2pKAALrsMfvpT9/HK\nK9CrV6yrEhERab005U9EpJ348EPXEn3/fsjKgilTYl2RiIhI6xc3I1TGmFuNMduMMcXGmHeNMXVO\nQDHGjDfGvG2MOWCMKTLG5Bpj/qcl6xURaU3+9jfXEj0pCdavV5gSERFpKnERqIwxVwJzgbuBM4AP\ngOXGmLomohQCvwPOAUYB9wKzjTH/rwXKFRFpNUpL4Yc/hG9/G6ZOhTVrYMSIWFclIiLSdsRFoAJm\nAI9ba5+y1m4EbgKKgBsinWyt3WCtfdpam2ut3Wmt/TuwHBewREQE2L0bzj/ftUNfuBCefNKNUImI\niEjTiXmgMsZ4gQzgtYpj1loLrATG1fMaZ4TOfaMZShQRaXVefx3S090+U2+9BTffrP2lREREmkPM\nAxXQC0gA9tU4vg/od7wnGmN2GWNKgLXAAmvtE81ToohI62AtPPggTJgAp57qWqJ/6UuxrkpERKTt\nau1d/r4CdAG+BPzGGLPZWvt0jGsSEWkx1lpMaOjpyBG4/nr45z/hJz+Be++FDq39f3kREZE4Fw/f\nag8A5UDfGsf7AnuP90Rr7Y7QzY+NMf2Ae4DjBqoZM2aQkpISdmzatGlMmzatASWLiMSO3+9n5syH\nWLp0NYFAMl5vIePHj+e99+7k8899/OtfcOmlsa5SREQkPixevJjFixeHHSsoKGiy6xu3XCm2jDHv\nAu9Za28P3TfATuARa+2D9bzGL4DvWmuH1fF4OpCdnZ1Nenp6E1UuItKy/H4/48ZNJTf3DoLBSYAB\nLLCcTp3msWbNEs44wxfjKkVEROJbTk4OGRkZABnW2pzGXCse1lABzANuNMZca4wZBTwGJAGLAIwx\n9xljnqw42RhzizHmG8aYEaGP7wE/Av4Sg9pFRFrMzJkPhcLUZFyYIvR5MoHADBYtmhvD6kRERNqf\nuAhU1tpngDuBXwHvA6cDk6y1+0On9AMGVXuKB7gvdO464Gbgx9bau1usaBGRFlZQAM89tzo0MlVb\nMDiZrKzVLVyViIhI+xYPa6gAsNYuBBbW8dj1Ne4/CjzaEnWJiLSkYNC1Ot+4sfbH3r0WSKZqZKom\nQyCQFNaoQkRERJpX3AQqEZH2pKgI/vtfF5Q2baoKTZs2QXGxO6dzZxg5EkaNgnPPhVGjDD/6USF5\neZbIocri9RYqTImIiLQgBSoRkWZiLezbF3m0aceOqvP69nWh6eyz4dpr3e1Ro2DwYEhICL/mmjXj\nWbBgeWgNVTiPZxmZmV9p5nclIiIi1SlQiYg0UmkpbNlSOzRt2uTWPYHbD2r4cBeUrrqqKjSdcgp0\n717/15oz505WrZpKbq6t1pjC4vEsIy3tYWbPXtIcb1FERETqoEAlIlJP+fm1p+ht3OjCVHm5Oycl\nBdLSYPRouPzyquA0bBh4vY2vwefzsWbNEmbNmktW1jwCgSS83iIyM8cze/YSfD61TBcREWlJClQi\nItWUl7vpeJGm6e0P9R01BoYOdUHp6193o0wVwalPH/d4c/L5fMyffw/z56MGFCIiIjGmQCUi7dLR\no7VHmjZtco0ijh1z5yQlVYWlCROqQtPJJ0NiYmzrr6AwJSIiElsKVCISF5pjpMVa2L27dnDauBE+\n+6zqvAEDXFA65xy48caq4DRwIHjiYrc+ERERiVcKVCISM36/n5kzH2Lp0tUEAsl4vYVMmTKeOXPu\nbNBaoJIS2Lw5clOIo0fdOV5vVQvy6p30TjkFunZtpjcoIiIibZ4ClYjEhN/vZ9y4qeTm3kEweA8V\n3eoWLFjOqlVTWbMmvMGCtXDgQO3AtHEjbNvmNsQF6NnTBaUxY8K76Q0d6jrtiYiIiDQl/XghIjEx\nc+ZDoTBVfT8lQzA4mdxcy9VXz+Xcc+8JC1D5+e4sj8d1zRs1Ci67LHy0qVevmLwdERERaacUqEQk\nJpYuXR0amaotGJzMiy/O4803q8LSxRdX3R4xAjp1atl6RURERCJRoBKRFmUtrF9vOXAgGTfNLxJD\nv35J7N5t8XjUxU5ERETilwKViDS70lJ48014/nl44QXYvdvg8RQClsihytK5c6HClIiIiMQ9NQQW\nkWZx5Ag88wxcc43b7HbiRHjpJbjiCnj9dbj55vF4PMsjPtfjWUZm5ldauGIRERGRhtMIlYg0mb17\nISvLjUS99pobmRozBmbMgEsvhdNPh4qtpjIy7uSNN6aSm2tDjSlclz+PZxlpaQ8ze/aSWL4VERER\nkXpRoBKRRtm0yQWo55+H995zHfjOPRceeAAuucS1K4/E5/OxZs0SZs2aS1bWPAKBJLzeIjIzxzN7\n9pIG7UMlIiIiEisKVCLSIMEgrF3r1kI9/7xrZ56UBJMmwaJF8PWvu72g6sPn8zF//j3Mnw/WWozR\nmikRERFpXRSoROSEjh1z654qmkrs3ev2e8rMdCNREyZAYmLjXkNhSkRERFojBSoRiaigAF5+2YWo\nV14Bv99tpnv11W491Je/DAkJsa5SREREJLYUqESk0u7dbgTqhRfciFQgABkZcNddLkR94QtVTSVE\nRERERIFKpF2zFnJzq5pKrFsHHTrA+efDww+7KX2DBsW6ShEREZH4pUAl0s6Ul8O771aFqM2boUsX\n+NrX4Pbb4eKLoXv3WFcpIiIi0jooUIm0AyUlsHKlC1BLl8Lnn0Pfvm4Eav58uOAC6Nw51lWKiIiI\ntD4KVCJt1KFD8NJLLkQtWwaFhXDyyfDd77r1UGef7faMEhEREZHoKVCJtCE7d1btD/Xmm25639ln\nw8yZLkSNGqWmEiIiIiJNSYFKpBWzFj78sGp/qJwc8HrdFL5HH3VT+gYMiHWVIiIiIm2XApVIK1NW\nBqtXV41EbdsGPh98/euuvfnkyZCSEusqRURERNoHBSqRVqCoCF59taqpxMGD0L8/XHKJm8p3/vnQ\nqVOsqxQRERFpfxSoROLUgQPw4osuRK1YAcXFkJYG3/++C1FnnqmmEiIiIiKxpkAlEke2bauayvfv\nf7s1UuPGwS9/6UajRo6MdYUiIiIiUl3cBCpjzK3AnUA/4APgh9badXWcexlwMzAG6AR8DNxjrV3R\nQuWKHJe1FlOPdnrWwoYNVZvs/uc/0LEjXHQRPPYYTJkC/fq1QMEiIiJxor7fQ0XiRVxMGDLGXAnM\nBe4GzsAFquXGmF51POVcYAXwNSAdeB1Yaoz5YguUKxKR3+9n+vS7SU2dwKBBl5KaOoHp0+/G7/eH\nnRcIwKpVMH06DB0K6eluc93TToNnn62a6nfjjQpTIiLSPvj9fqbfNZ3U9FQGnTWI1PRUpt81vdb3\nUJF4ZKy1sa4BY8y7wHvW2ttD9w2wC3jEWvtAPa/xEfAPa+3sOh5PB7Kzs7NJT09vospFHL/fz7hx\nU8nNvYNgcBJgAIvHs5y0tHm8+uoS3nnHxwsvuLB06BCcdJJbC3XJJXDeea7duYiISHvj9/sZN3Ec\nuSNyCQ4PVnwLxbPVQ9qnaaxZsQafzxfrMqWNycnJISMjAyDDWpvTmGvFfMqfMcYLZAC/rjhmrbXG\nmJXAuHpewwA+IL9ZihQ5gZkzHwqFqcnVjhqCwcl8/LHlpJPmEgzew2mnwa23uiCVnq5NdkVE2jJN\nXaufmffOdGFqRLDqoIHg8CC5NpdZs2cx/zfzY1egyAnEPFABvYAEYF+N4/uAU+p5jR8DycAzTViX\nSL0tXbqaYPCeOh6dTLdu81i7FoYPb8mqRESkpfn9fmbeO5OlK5cSSAjgLfcyZcIU5vx8TpsbZbHW\nEggGKCwtpChQRGGgsM7bRYEiCksLI95e/sJygtOCEV8jODzIk88+ybDLhtEjsQc9k3rSM7Fn5e1u\nnbvhMXGxgkXasXgIVI1ijLka+DmQaa09cKLzZ8yYQUqNXU+nTZvGtGnTmqlCaYtKS10DiXXrYO1a\ny2efJePmKERiSExMYtgwe5xzRESktQubupZZNXVtwdYFrJq4qsWnrllrKS0vDQsxJwo+tULQCc4t\nt+UnrMNgSO6YTJI3iWRv6HPHZJK9ySR2SAQvx/sWij/o56ev/ZTisuKI1+6e2D0sZFXejnQsdLtL\nxy5tavRQo6HHt3jxYhYvXhx2rKCgoMmuH/M1VKEpf0XAVGttVrXji4AUa+1lx3nuVcCfgCustctO\n8DpaQyVRCQZh40YXnio+NmxwoSohwTWT2Lx5AkePvkrk7wiWoUMvYtu2lS1duoiItKDpd01nQd6C\n8KlrIZ7NHm4bcFvY1DVrLcfKjx139Oa4wafsxOc2JPDUDDu1bnuTw4JRXSGp5u3OHTof94f91PRU\ntmdur+tbKEOzhrItZxvFgWLyi/PJL87nYPFBDhYdrLydX5zPwaKDVberPR4IBmpd1uvx0iOxR8QQ\n1jOp7kCW6E084Z9nS2lPo6HNoU2tobLWBowx2cCFQBZUrom6EHikrucZY6bhwtSVJwpTIvVlLezY\nER6esrOhosnQyJFw1llwzTUwdiyMGQOJiTB9+ngWLFheYw2V4/EsIzPzKy38TkREpCUdKzvGP1f8\nk+CldU9d+/3ff8+LA18MC0FBG/n86jzGc9zQ0jupd4MCTs3bnRI6xXR0Y8qEKSzYusA1pKjBs8VD\n5kWZACR6ExnoHcjArgPrfW1rLUdLj0YMYTUDWe6B3Mpjh0oORfy7SeyQWDtw1QhhNR/vkdgDb0LT\ndp6Kt9HQ9i7mgSpkHrAoFKzWAjOAJGARgDHmPmCAtfa60P2rQ49NB9YZY/qGrlNsrT3SsqVLa/b5\n5+Hhad062L/fPXbSSS48/exnLjxlZEC3bpGvM2fOnaxaNZXcXBsKVRVd/paRlvYws2cvaam3EQ4X\nJAAAHbNJREFUJCIizcBaS35xPlsObWHroa1syQ99PrSFLYe28FnBZ1DCcaeudezUkW+mfdMFmnqG\nnSRvUswDT3Ob8/M5rJq4ilxbo8vfFg9pm9OYvTBiA+d6Mcbg6+TD18nHkG5D6v28oA1yuORw5chX\nxEAWCmLbD2+vPHbkWOQfQ7t26lr3CFgdgex468PUyCO+xHzKXwVjzC3AXUBfYANuY9/1oceeAIZY\nay8I3X8dtxdVTU9aa2+o4/rpQHb//mO54oqvMWfOnUru7cyRI260qXp42rHDPdajhwtPY8dWfTR0\nDyi/38+sWXPJylpNIJCE11tEZuZ4Zs/+kb7WRERagbJgGbsKdoWHpsPu85ZDW8J+WO6Z2JNh3Ycx\nvMdwhncfzrDuw5h53Uz2Xr73hFPXpDa/38+s2bPIWplFwBPAG/SSOSGT2bNmt6rvoYHyAIdKDoVP\nP6wZyEryaz1+ovVhYYErsSeL7ljE4W8drvtrbelQtmXra+14mnLKX9wEquZWEahgPR7PftLS5rFm\nzZJW9Y9U6q+kBD74oCo4rV0Lmza5KX3JyW60qXp4Sk1t2hbmWhwqIhKf/Mf8lSNLNUPTjoIdlAXL\nADfNbkjKEBeaQoGpenhK6ZxS69rT75rOgr11TF2LsIZKImuP30Mr1odFXA9WY1TsYNFBNj26ieBV\ndU8X7bKkC48+9Shf6PMFRvUaRZeOXVrw3bQOClRRqApU2UA6Hs8r3Hbbe8yff0+MK5PGKi+HTz4J\nD08ffgiBgNss94tfDA9PaWmumYSIiLQ9QRskz58XHpoObamcore/aH/luV06dqkKSzVC0+CUwQ1e\n91LnBrWhqWta1yJN5USNPBL+mkD5d6oakgzqOoi03mmM7jWatN5ppPVKI613Gr2SerVYzfFGgSoK\nNQOV67w2kW3bXo1xZdIQ1sLWreHhKScHiorcCFNaWnh4+uIXoVOnWFctIiJNqaSshG2HttUKTVsP\nbWXroa2UlJVUnjvAN6DO0NQrqVeTj4S0lalrEt/qMxo65945bDywkdz9uXyy/xNyD+SSeyCXLflb\nKrs/9krqxejeo13ACoWs0b1HM9A3sM2PEipQRaF2oAKP5xLGjHmegQMNAwfCgAGEfR44ELp3b9qp\nYNIweXnh4Wn9esjPd48NHVoVnM46C9LTQd+rRERaP2stB4sPhjV+qP5595HdWNzPL50SOpHaPTVi\naErtlhrTNtftceqatIzGjIYeKzvGp/mfkrs/tzJk5e7PZeOBjRwrPwaAr6OPUb1GVY5mVYSu1O6p\ndPDES0+7xmlTbdNjx9K1ayEZGYbdu+Hdd2H37qoObxU6d3YBq3rIqhm8BgxwrbOlcQ4fdoGpIjyt\nW+f+TgD69HHBafp0F57OPBN6945tvSIi7UlTh4OyYBk7C3bWGZpqNoCoGFU6Z/A5YaFpgG9AnZ3Q\nYk1hSpqLz+djzYo1bjR0aY3R0IXHHw3t1KETp/Y5lVP7nBp2vDxYzvbD2ysD1icHPiF3fy7Pb3y+\n8t9jx4SOjOw5stao1sieI+ncoXOzvud41m5HqOpaQ1Va6kZFdu+GPXvCP1e/XVgYfv3u3WuPbtUM\nXn36aO1OheJieP/98PD06afuMZ/PBaaKkaexY2HQII0UikjT0KhB/TV249Ajx47UajFe8XnH4R2V\n044STAKDUwYzvMdwhnULb/5QVwMIEanSnP+vWWvJO5rnpg3WGNXaV7gPcE1chnUfVmvq4Kheo+ja\nqWuz1NVYmvIXhfAuf5+TlvZw1F3+rHUbvdYMWTWDV16ea5hQISHBteKua3phxe2uXeMnPDTFP9BA\nAD7+ODw8ffSR+7Pp1Mltjlu9ZfnIkeCJz182ikgr1dhg0B7VOaVoq4e0T92UouQuyeT588KaPlQP\nTQeKDlRer6IBRKTQFE0DCBGJvfzi/KqQVW1Ua0fBjspzBvoGRmyI0Tupd0x/uaVAFYWqfajO4pvf\n/FqL7A1UXu6mENY1ylXxuWJNUIXk5MjTCqsHr/79m6/Zgt/vZ+bMh1i6dDWBQDJebyFTpoyv195d\nwSBs3hwent5/37Ux93jgC18ID0+nngodOzbP+xARgfoFg/Yeqqy1BIIBSstLCZQHCAQD/GTmT3jy\n4JPhG4dW+BS6H+xO8VeKIzaAqB6aKtY1NUcDCBGJT4WlhWw6uKnWqNbm/M2VWxP0SOxRa+pgWq80\nBqUMapFpvApUUagIVNnZ2aSnp8e6nDDFxbWnGUYKXiUl4c/r1avu6YUVt3v1athoj9/vZ9y4qeTm\n3kEwOImKnzw8nuW19u6y1tVVEZzWrXNroAoK3LWGDw8PT2ec4cKiiEhLmn7XdBbkLYgYDJpqb6Cg\nDVYGkeqhpD63A+Wh+9HcbqJrVUy9C/MkcC11tmX2PePj3v+7tzI0xboBhIjEv9LyUjbnb47YEKNi\nc+Nkb3LEhhjDewxv0oYYClRRiOdAVR/WwqFDtUNWzQC2d687t4LX60azTrS+q0tov7fp0+9mwYJx\nBIOTa9Xg8bzClCnvkZFxT2WA2rvXPda/f3h4OvNM6NGjBf5gpM3QuhZpCtZajpYe5VDJIQ4VH+JQ\nySG+NeVb7J+6v85gkPx0MpN+OalRwSdiIImSwdAxoSPeBC9ej7det70Jofs1b9fz+TVvd/B04HvT\nvsfBSw/WWefAFweya+0u/bsVkUYL2iA7Du+omjpYrc374ZLDAHg9Xk7ueXKtUa1Tep7SoF/mVEwB\nfy7rOfI25YG6/LUfxriA0qOHmyZXl7Iy2Lev7lGuTz5xtytGkSp07erC1fbtqwkG74l47WBwMi+8\nMI8333Sh6YYbqgLUwIFN916l/dC6lsZrq0G0OFDMoZJD5BfnVwaj6rcPFR8ivyTyYxXTSQCwQIDI\nYQp3POAJcPTYUTp26Ehih0RSOqWEB43jBZZmuJ3giY/uRf9j/oeD9mCdQdRb7m2TX3si0vI8xkNq\n91RSu6dy8ckXVx631rKvcF+tqYN/yvkTeUfzAPdLqNTuqbUaYqT1SqvV0CZsCvh5QdjUNPUrULUx\nHTpUjUIdT2Fh7dGtzz6z/OEPyRzvJ4++fZPYs8fi8eibqDRO2H9qmVXrWhZsXcCqiau0ruU4WksQ\nLS0vjRh4woJRHY9V7IVSU5I3iR6JPejeuTvdE7vTvXN30nqlVd6v+ViPxB5c+MKF7LK76gwGAzoN\nYPl3ljfvH0YrNGXCFBZsrWPj0C0eMi/KjEFVItKeGGPo16Uf/br044LUC8IeO1xyuFZDjCW5S9i+\nZnvlPnX9u/QPmzq48s8r3c8dI4Kwpwnr1JQ/qS41dQLbt79KXT95DB16Edu2rWzpsqQNaol1LW1R\nSzdYKAuWcbjkcN2jRMX5EYPRoeJDFAYKI16zU0KnsMATdrtGGKp+u1vnbnTq0PBuPNPvms6CvXUE\nA32t1akxG4eKiMRKUaCI/x78b61RrU8PfkrgiUDV2tA9wB8ATfmTpjZlyngWLFhexxqqZWRmfiUG\nVUlbtHTlUjcyFUFweJC/PPcXBl8ymA6eDi364TGeuJ7GNPPemVW/Xatg3J9Zrs1l1uxZtcJB0AY5\ncuxI3WHoOMGo+uaq1XXwdKgVfk7qehKn9TnthMGopRsXzPn5HFZNXEWujRwMZi+c3aL1tBaN2ThU\nRCRWkrxJjOk3hjH9xoQdLy0rZdDzg/jcfN7kr6kRKglT1eVvRihUVXT5W9aovbtEKhSWFvLG9je4\n8oorKZwaeQQDwPzD0OXaLpTbcsqCZZQFywjayAGsqdUVthJMQosHvJofP732pxy44kDdDRb+kcyX\nZ345LCgVHCuI+GdnMJVBJ9Io0fGCUZeOXeI6eNbk9/tdMFhZIxjMUjCor7a6Xk9E2o/U9FS2Z27X\nCFVjfePqb3BF5hVxt9YgXvh8PtasWcKsWXPJyppHIJCE11tEZuZ4Zs9WmJKGC9og/9n3H1ZsWcHy\nLct5e+fblJaXklCU4BoG1BEMhiQOYdtPt9W6VnmwKmBV/6gevOLto6SspEmuUx4sh7I6/sxwx8sS\nyvB19DG029ATBqOunbq2yF4f8cDn8zH/N/OZz3wFgyjpz0xEWrvjrQ1tjHY3QsX3wVOszRzrSz94\nSDT2Hd3Hq1tfZfmW5by65VX2Fe4jyZvE+UPPZ9LwSUwcPpGFDyzUupYGstaSmpHKjswddQbRoVlD\n2ZazLcKDIiIi7VvY2tCkYJONULXLQMUA/cAm0pSOlR3j7Z1vs2LLClZsXcGGvRsAGNNvTGWAGj9o\nfFhDAS14j44aLIiIiESvYgr4s1nPkrexafahareBCgspz6Rw/1P3MzhlMENShjA4ZTC+TvoBTuRE\nrLVsOriJ5ZuXs3zLct7c8SZFgSL6Jvdl4vCJTBw+kYuGXUTfLn2Pex2ta2k4BVEREZHGy8nJISMj\nAxSo6q9WoAIS/pGAvcoSpOo3vd07d3cBq9uQypBV+bnbEPok92k3aw5Eqssvzue1ra9VroXadWQX\nHRM6cs7gc5g4fCKThk/itL6nRf3vQ9NL609BVEREpHEUqKIQaYRqaNZQNq/fzB7/HnYW7GRHwQ73\n+fCOqtsFOzhaerTyOp0SOjEoZVDEsDUkZQgndT0pqn1SROJNWbCM9z57j+VblrNiywrW7VlH0AZJ\n65VWGaDOHXIuyR2TY11qu6YgKiIi0nBNGajaXZe/ChW7vCd4EhiUMohBKYMYz/ha51lrOVxymB0F\nO9hxeEdY8Pp4/8e8/OnL7CvcV3m+we3oXBGyBnetMdrVbQgpnVL0A5DEpW2HtlUGqNe2vcaRY0fo\n3rk7E4ZN4Mb0G7lo+EUMThkc6zKlGv1fIiIiElvtMlB5Ntd/M0djQvu0JHavtUFYhZKyEnYV7KoM\nWzsO72DnETfSlb0nm50FOwkEA5Xn+zr66pxSODhlMP279CfBk9Bk71ekLv5jfl7f/nrlNL7N+ZtJ\nMAmMGzSOO8fdycThEzlzwJn6ehQRERGpQ7ub8td/VH++mfnNFl1rELRB9h3dF3GUq+JYwbGCyvM7\neDowqOugOke5BqcMJtGb2CK1azpR2xK0QXLycioD1Du73qEsWEZqt1QmDZ/EpBGT+OrQr5LSOSXW\npYqIiIg0G62hikJFoMrOziY9PT3W5dRSUFLAzoKdEUe5dhbsZI9/D5aqv6veSb0rR7SGpAypNcrV\nM7Fn1EHI7/cz896ZLF25lEBCAG+5lykTpmgz5FZq95HdYXtCHSw+SJeOXbgg9YLKluYjeoyIdZki\nIiIiLUZrqNqglM4pnNb5NE7re1rEx0vLS/nsyGeVTTOqj3K99OlL7CzYSUlZSeX5Sd6kOqcUDkkZ\nwsCuA+ngqf3XH9aSObOqJfOCrQtYNXGVWjK3AsWBYv69898s37ycFVtX8NHnH2EwZAzI4KYzb2Li\n8ImMO2kc3gRvrEsVERERafUUqFqJjgkdGdZ9GMO6D4v4uLWW/UX7w8JWxSjXuj3rWJK7hIPFByvP\n9xgPA30Da41yvfyHl12YGlFt01ADweFBcm0us2bP0qahccZay8f7P64MUG/teIuSshIG+AYwafgk\nZp4zkwnDJtArqVesSxURERFpcxSo2ghjDH2S+9AnuQ9jB46NeM7R0qNV0wprjHK9vfNtdh/ZTfnK\ncrg28msEhwf563N/5bzvnkf/Lv0Z4BtAvy791CY+Bg4UHeDVLa+yYusKVmxZwR7/Hjp36Mx5Q87j\n1xf8monDJzK692itfxMRERFpZgpU7UiXjl0Y3Xs0o3uPjvh4oDzA4KzB7DV7I1/AQH5ZPlOfnuqm\nAob0SOxRGbD6+/pX3e7Sv/J+f19/krxJzfCu2ofS8lLW7FpT2UwiJy8Hi+W0Pqdx9alXM3H4RM4Z\ncg6dO3SOdakiIiIi7YoClVTyJnjpHOwMlrDAVMnC0KShrP3xWvKO5pHnzyPvaB57/Hsqb2/O38xb\nO94iz5/HsfJjYU9P6ZRSZ+CqHsZ8nbRGy1rL5vzNlXtCvb79dY6WHqVXUi8mDp/ID8/6IRcNv4gB\nvgGxLlVERESkXYubQGWMuRW4E+gHfAD80Fq7ro5z+wFzgTOBEcB8a+0dLVVrWzZlwhQWbF1AcHiw\n1mMVmyH3Tu5N7+TenN739DqvU7Ehcs3AlefPY8/RPews2Ml7u99jj38PRYGisOd26dildtiKEL7i\neYPkxYsXM23atAY9p6CkgNe2vVY5CrX98Ha8Hi/jB4/nZ1/5GZNGTGJMvzF4jKeZqpbWKJqvNZFo\n6GtNWoq+1qS1iYtAZYy5EheQvg+sBWYAy40xI621ByI8pRPwOXBv6FxpInN+PodVE1eRa3NdqAp1\n+fNsqf9myBC+IXJdUwzBBS9/qT/iaFfF/Q17N5B3NI8jx46EPbdzh86Rw1aN+z0Se7R48KrPN4Py\nYDnr9qxjxRa3Durdz96l3JYzsudIvnHyN5g0YhLnDTlPI3ZyXPrBQ1qKvtakpehrTVqbuAhUuFD0\nuLX2KQBjzE3A14EbgAdqnmyt3RF6DsaY77VgnW2ez+djzYo1zJo9i6ylWQQ8AbxBL5kTMpm9sOk3\nQzbG0LVTV7p26sopvU457rmFpYVhUw3z/KEAFgpfuftz2ePfw6GSQ2HP65jQkX5d+tUKXDXDV+/k\n3o0a/am+f9ferXtJTU+ttX/XzoKdlSNQr219jUMlh0jplMKFwy5k4dcXMnH4RIZ2Gxp1DSIiIiLS\nsmIeqIwxXiAD+HXFMWutNcasBMbFrLB2zOfzMf8385nPfKy1cTOtLrljMiN6jDjhJrQlZSXsPbo3\n4lTDPH8eb+96mzx/HvuL9oc9r4OnA32T+0Yc7ap+u09yn1p7eNXav2sxbM/czoKtC3jh/Be4+GcX\n88beN9h4YCMe4+HsgWcz/ezpTBw+kbMGnhVxTzARERERiX/x8FNcLyAB2Ffj+D7g+EMW0uziJUw1\nROcOnRnabegJR3pKy0vZd3Rf7dGuUAhbu9s13/i88HOCtmpNmcd46JPcJ2x06z//+A+fjPgEO8JW\nvUBo/66dwZ389Q9/Zdpt05j91dlckHoB3RO7N9O7FxEREZGWFA+BqqV0BsjNzY11HRJnOtCBQQxi\nUMIgSMF9VFMWLONQ8SEOFB1gf9F+97nQfT6Qf4A1hWvY+O+N2Ist7Ak9qYSq28ngy/Zx08CboAS2\n5W5jG9ta7g1Km1ZQUEBOTk6sy5B2QF9r0lL0tSYtoVomaPSeM8Zae+KzmlFoyl8RMNVam1Xt+CIg\nxVp72Qme/zrw/om6/Bljrgb+1viKRURERESkjbjGWvv3xlwg5iNU1tqAMSYbuBDIAjBuntmFwCNN\n+FLLgWuA7bjxAxERERERaZ86A0NxGaFRYh6oQuYBi0LBqqJtehKwCMAYcx8wwFp7XcUTjDFfxDX1\n7gL0Dt0vtdZGnNNnrT0INCp9ioiIiIhIm/FOU1wkLgKVtfYZY0wv4FdAX2ADMMlaW9GGrR8wqMbT\n3gcq5iumA1cDO4BhzV+xiIiIiIhIHKyhEhERERERaa2i38VURERERESknVOgEhERERERiVK7CFTG\nmFuNMduMMcXGmHeNMWNjXZO0LcaYnxpj1hpjjhhj9hlj/mWMGRnruqTtM8b8xBgTNMbMi3Ut0vYY\nYwYYY/5ijDlgjCkyxnxgjEmPdV3SthhjPMaYe40xW0NfZ5uNMbNiXZe0fsaYc4wxWcaY3aHvlZkR\nzvmVMWZP6GvvVWPMiIa+TpsPVMaYK4G5wN3AGcAHwPJQEwyRpnIO8DvgbGAC4AVWGGMSY1qVtGmh\nXw59H/f/mkiTMsZ0A1YDx4BJQBrwI+BQLOuSNuknwA+AW4BRwF3AXcaY22JalbQFybhmd7dQ1cyu\nkjHmf4HbcN9LzwIKcTmhY0NepM03pTDGvAu8Z629PXTfALuAR6y1D8S0OGmzQoH9c+Bca+3bsa5H\n2h5jTBcgG7gZ+Dn12OBcpCGMMfcD46y158W6FmnbjDFLgb3W2hurHXsOKLLWXhu7yqQtMcYEgUut\ntVnVju0BHrTWPhy63xXYB1xnrX2mvtdu0yNUxhgvkAG8VnHMugS5EhgXq7qkXeiG+01IfqwLkTZr\nAbDUWrsq1oVImzUFWG+MeSY0lTnHGPP/Yl2UtEnvABcaY06Gyr1GxwMvx7QqadOMMam4rZmq54Qj\nwHs0MCfExT5UzagXkIBLmtXtA05p+XKkPQiNgv4WeNta+0ms65G2xxhzFTAGODPWtUibNgw3AjoX\nmIObDvOIMeaYtfYvMa1M2pr7ga7ARmNMOe4X/jOttf+IbVnSxvXD/fI7Uk7o15ALtfVAJRILC4HR\nuN+uiTQpY8xJuMA+wVobiHU90qZ5gLXW2p+H7n9gjDkVuAlQoJKmdCVwNXAV8AnuF0bzjTF7FN6l\nNWjTU/6AA0A50LfG8b7A3pYvR9o6Y8yjwMXA+dbavFjXI21SBtAbyDHGBIwxAeA84HZjTGlohFSk\nKeQBuTWO5QKDY1CLtG0PAPdba5+11n5srf0b8DDw0xjXJW3bXsDQBDmhTQeq0G9vs4ELK46Ffti4\nEDdfV6TJhMLUJcBXrbU7Y12PtFkr4f+3dychctRhGMafF0VwwQUlHgWNuxIwOYgLOXjRAQURFMGF\niEpwQeIpyrhgNKCowYNelKBGBJeLy00wog7uRnEXSYzGCBHjljEuIZ+HqpFmHIxTJumenucHRTe1\nfnVout6qr//NiTR3cOe109vAY8C8GvaRhrQ7jfHP9vijgfV9qEXDbR+aG+C9tjPk16nqr6paRxOc\nenPC/jQjNk8rJ8yGlr97gYeTvAO8CSyh+eA+3M+iNFySPABcCJwDjCeZuNvxU1X91r/KNGyqapym\nJeZvScaB76tq8tME6f9YAYwluQF4kuYi43Lgin/dSpq+54DRJBuAj4CTaK7XHuprVZrxkuwLzKV5\nEgVweDvoyeaq+pqmhX40yRfAl8AyYAPwzLSOMxtuZia5iuY/DQ6lGYv+2qp6u79VaZi0Q3FO9WFa\nVFWP7u56NLskeRF4z2HTtbMlGaEZMGAusA64p6pW9rcqDZv2oncZcC4wB9gIPA4sq6pt/axNM1uS\nhcBq/nmN9khVXdaucyvN/1AdCLwCXF1VX0zrOLMhUEmSJEnSrmBvqiRJkiR1ZKCSJEmSpI4MVJIk\nSZLUkYFKkiRJkjoyUEmSJElSRwYqSZIkSerIQCVJkiRJHRmoJEmSJKkjA5UkSTuQZGGS7Un273ct\nkqTBYqCSJOm/qX4XIEkaPAYqSZIkSerIQCVJGnhp3JBkbZJfk6xJcl67bKIdbyTJ+0m2JnktyfGT\n9nFekg+T/JZkXZLrJy3fK8mdSb5q1/k8yaJJpSxI8laS8SRjSY7cxacuSRpwBipJ0kxwI3ARcCVw\nHLACWJXk9J517gKWAAuA74Bnk+wBkGQ+8ATwOHACcAuwLMklPduvAi4ArgGOAS4HtvQsD3B7e4z5\nwDZg5U49S0nSjJMqW8IlSYMryV7AZuCMqnqjZ/6DwN7Ag8Bq4PyqerpddhCwAbi0qp5O8hhwSFWd\n2bP9ncBIVZ2Y5Cjg0/YYq6eoYSHwYrv8pXbeWcDzwN5V9ccuOHVJ0gzgEypJ0qCbC+wDvJDkl4kJ\nuBg4ol2ngNcnNqiqH4DPgGPbWccCY5P2OwYcmSTAPJonTi/voJYPet5/277Omd7pSJKGyZ79LkCS\npB3Yr30dATZOWvY7TeD6v7b+x/X+7Hk/0eLhzUlJmsX8EpAkDbqPaYLTYVW1dtL0TbtOgJMnNmhb\n/o5qtwX4BDh10n5PAz6vpvf9A5rvxIW78DwkSUPIJ1SSpIFWVVuS3A2saAeZeBU4gCYg/QR81a56\nc5LNwCbgDpqBKZ5pl90DvJlklGZwilOAq4HF7THWJ3kUWJnkOuB94DBgTlU91e4jU5Q31TxJ0ixi\noJIkDbyquinJJmApcDjwI/AusBzYg6b9bilwH00L4Brg7Kra1m6/Jsn5wG3AKM3vn0aralXPYRa3\n+7sfOJgmqC3vLWOq0nbWOUqSZiZH+ZMkzWg9I/AdVFU/97seSdLs4m+oJEnDwNY7SVJfGKgkScPA\ndgtJUl/Y8idJkiRJHfmESpIkSZI6MlBJkiRJUkcGKkmSJEnqyEAlSZIkSR0ZqCRJkiSpIwOVJEmS\nJHVkoJIkSZKkjgxUkiRJktSRgUqSJEmSOvoL/KlKVHcwzYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a29d061d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304660\n",
      "(Epoch 0 / 1) train acc: 0.131000; val_acc: 0.146000\n",
      "(Iteration 21 / 980) loss: 2.209419\n",
      "(Iteration 41 / 980) loss: 2.150775\n",
      "(Iteration 61 / 980) loss: 1.983743\n",
      "(Iteration 81 / 980) loss: 1.552287\n",
      "(Iteration 101 / 980) loss: 1.884865\n",
      "(Iteration 121 / 980) loss: 1.729202\n",
      "(Iteration 141 / 980) loss: 1.972821\n",
      "(Iteration 161 / 980) loss: 1.735250\n",
      "(Iteration 181 / 980) loss: 1.826601\n",
      "(Iteration 201 / 980) loss: 1.631622\n",
      "(Iteration 221 / 980) loss: 1.576881\n",
      "(Iteration 241 / 980) loss: 1.706221\n",
      "(Iteration 261 / 980) loss: 1.840073\n",
      "(Iteration 281 / 980) loss: 1.559024\n",
      "(Iteration 301 / 980) loss: 1.661518\n",
      "(Iteration 321 / 980) loss: 1.590668\n",
      "(Iteration 341 / 980) loss: 1.553137\n",
      "(Iteration 361 / 980) loss: 1.578101\n",
      "(Iteration 381 / 980) loss: 1.637798\n",
      "(Iteration 401 / 980) loss: 1.615778\n",
      "(Iteration 421 / 980) loss: 1.745806\n",
      "(Iteration 441 / 980) loss: 1.473506\n",
      "(Iteration 461 / 980) loss: 1.320376\n",
      "(Iteration 481 / 980) loss: 1.750080\n",
      "(Iteration 501 / 980) loss: 1.398131\n",
      "(Iteration 521 / 980) loss: 1.652622\n",
      "(Iteration 541 / 980) loss: 1.791312\n",
      "(Iteration 561 / 980) loss: 1.439398\n",
      "(Iteration 581 / 980) loss: 1.751751\n",
      "(Iteration 601 / 980) loss: 1.675722\n",
      "(Iteration 621 / 980) loss: 1.830303\n",
      "(Iteration 641 / 980) loss: 1.438314\n",
      "(Iteration 661 / 980) loss: 1.554001\n",
      "(Iteration 681 / 980) loss: 1.444878\n",
      "(Iteration 701 / 980) loss: 1.557787\n",
      "(Iteration 721 / 980) loss: 1.343517\n",
      "(Iteration 741 / 980) loss: 1.550125\n",
      "(Iteration 761 / 980) loss: 1.347113\n",
      "(Iteration 781 / 980) loss: 1.519804\n",
      "(Iteration 801 / 980) loss: 1.623981\n",
      "(Iteration 821 / 980) loss: 1.523775\n",
      "(Iteration 841 / 980) loss: 1.735336\n",
      "(Iteration 861 / 980) loss: 1.809287\n",
      "(Iteration 881 / 980) loss: 1.596757\n",
      "(Iteration 901 / 980) loss: 1.488831\n",
      "(Iteration 921 / 980) loss: 2.339850\n",
      "(Iteration 941 / 980) loss: 1.530015\n",
      "(Iteration 961 / 980) loss: 1.223430\n",
      "(Epoch 1 / 1) train acc: 0.463000; val_acc: 0.462000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2DepBkYWEP6AtYrrZ07OtRV8HW9ONf2Zmhbpu1i4ub5N5dv2su0ZpVg/tm730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\nuAAAQaFwAQCCQuECAASFwgUACMr/BVVtlOL3BJQtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a64834d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 10.00921254   9.60421614  11.61733623]\n",
      "  Stds:  [ 3.62797821  4.04256713  4.46467053]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ -2.22044605e-16  -1.72084569e-16   0.00000000e+00]\n",
      "  Stds:  [ 0.99999962  0.99999969  0.99999975]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.  7.  8.]\n",
      "  Stds:  [ 2.99999886  3.99999878  4.99999875]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print 'Before spatial batch normalization:'\n",
    "print '  Shape: ', x.shape\n",
    "print '  Means: ', x.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', x.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization:'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization (nontrivial gamma, beta):'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [ 0.06766818  0.01944965  0.08487383  0.02035362]\n",
      "  stds:  [ 1.01722492  1.01082582  1.0034756   0.98107791]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in xrange(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After spatial batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=(0, 2, 3))\n",
    "print '  stds: ', a_norm.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  7.59619497612e-08\n",
      "dgamma error:  2.77344083917e-12\n",
      "dbeta error:  1.92739652662e-11\n"
     ]
    }
   ],
   "source": [
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment!\n",
    "Experiment and try to get the best performance that you can on CIFAR-10 using a ConvNet. Here are some ideas to get you started:\n",
    "\n",
    "### Things you should try:\n",
    "- Filter size: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- Number of filters: Above we used 32 filters. Do more or fewer do better?\n",
    "- Batch normalization: Try adding spatial batch normalization after convolution layers and vanilla batch normalization aafter affine layers. Do your networks train faster?\n",
    "- Network architecture: The network above has two layers of trainable parameters. Can you do better with a deeper network? You can implement alternative architectures in the file `cs231n/classifiers/convnet.py`. Some good architectures to try include:\n",
    "    - [conv-relu-pool]xN - conv - relu - [affine]xM - [softmax or SVM]\n",
    "    - [conv-relu-pool]XN - [affine]XM - [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN - [affine]xM - [softmax or SVM]\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the course-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at least 65% accuracy on the validation set. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training, validation, and test set accuracies for your final trained network. In this notebook you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 9800) loss: 2.311459\n",
      "(Epoch 0 / 10) train acc: 0.094000; val_acc: 0.119000\n",
      "(Iteration 21 / 9800) loss: 2.221633\n",
      "(Iteration 41 / 9800) loss: 1.867802\n",
      "(Iteration 61 / 9800) loss: 2.084872\n",
      "(Iteration 81 / 9800) loss: 2.286923\n",
      "(Iteration 101 / 9800) loss: 1.844758\n",
      "(Iteration 121 / 9800) loss: 1.674374\n",
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      "(Iteration 161 / 9800) loss: 1.717647\n",
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      "(Iteration 201 / 9800) loss: 1.864889\n",
      "(Iteration 221 / 9800) loss: 1.547490\n",
      "(Iteration 241 / 9800) loss: 1.645704\n",
      "(Iteration 261 / 9800) loss: 1.579844\n",
      "(Iteration 281 / 9800) loss: 1.687198\n",
      "(Iteration 301 / 9800) loss: 1.534935\n",
      "(Iteration 321 / 9800) loss: 1.542697\n",
      "(Iteration 341 / 9800) loss: 1.927897\n",
      "(Iteration 361 / 9800) loss: 1.632217\n",
      "(Iteration 381 / 9800) loss: 1.744820\n",
      "(Iteration 401 / 9800) loss: 1.343114\n",
      "(Iteration 421 / 9800) loss: 1.745792\n",
      "(Iteration 441 / 9800) loss: 1.545797\n",
      "(Iteration 461 / 9800) loss: 1.530679\n",
      "(Iteration 481 / 9800) loss: 1.676910\n",
      "(Iteration 501 / 9800) loss: 1.647185\n",
      "(Iteration 521 / 9800) loss: 1.947325\n",
      "(Iteration 541 / 9800) loss: 1.680700\n",
      "(Iteration 561 / 9800) loss: 1.843356\n",
      "(Iteration 581 / 9800) loss: 1.756664\n",
      "(Iteration 601 / 9800) loss: 1.457374\n",
      "(Iteration 621 / 9800) loss: 1.477811\n",
      "(Iteration 641 / 9800) loss: 1.638420\n",
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      "(Iteration 681 / 9800) loss: 1.881321\n",
      "(Iteration 701 / 9800) loss: 1.514273\n",
      "(Iteration 721 / 9800) loss: 1.511105\n",
      "(Iteration 741 / 9800) loss: 1.708444\n",
      "(Iteration 761 / 9800) loss: 1.590162\n",
      "(Iteration 781 / 9800) loss: 1.472306\n",
      "(Iteration 801 / 9800) loss: 1.758018\n",
      "(Iteration 821 / 9800) loss: 1.616832\n",
      "(Iteration 841 / 9800) loss: 1.524076\n",
      "(Iteration 861 / 9800) loss: 1.363758\n",
      "(Iteration 881 / 9800) loss: 1.292456\n",
      "(Iteration 901 / 9800) loss: 1.592843\n",
      "(Iteration 921 / 9800) loss: 1.398381\n",
      "(Iteration 941 / 9800) loss: 1.413307\n",
      "(Iteration 961 / 9800) loss: 1.204163\n",
      "(Epoch 1 / 10) train acc: 0.506000; val_acc: 0.493000\n",
      "(Iteration 981 / 9800) loss: 1.382140\n",
      "(Iteration 1001 / 9800) loss: 1.560785\n",
      "(Iteration 1021 / 9800) loss: 1.499642\n",
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      "(Iteration 1181 / 9800) loss: 1.716714\n",
      "(Iteration 1201 / 9800) loss: 1.460127\n",
      "(Iteration 1221 / 9800) loss: 1.439299\n",
      "(Iteration 1241 / 9800) loss: 1.435835\n",
      "(Iteration 1261 / 9800) loss: 1.601247\n",
      "(Iteration 1281 / 9800) loss: 1.405328\n",
      "(Iteration 1301 / 9800) loss: 1.379965\n",
      "(Iteration 1321 / 9800) loss: 1.221449\n",
      "(Iteration 1341 / 9800) loss: 1.367157\n",
      "(Iteration 1361 / 9800) loss: 1.412334\n",
      "(Iteration 1381 / 9800) loss: 1.609093\n",
      "(Iteration 1401 / 9800) loss: 1.640086\n",
      "(Iteration 1421 / 9800) loss: 1.734009\n",
      "(Iteration 1441 / 9800) loss: 1.510003\n",
      "(Iteration 1461 / 9800) loss: 1.558881\n",
      "(Iteration 1481 / 9800) loss: 1.584549\n",
      "(Iteration 1501 / 9800) loss: 1.242816\n",
      "(Iteration 1521 / 9800) loss: 1.505605\n",
      "(Iteration 1541 / 9800) loss: 1.381565\n",
      "(Iteration 1561 / 9800) loss: 1.213622\n",
      "(Iteration 1581 / 9800) loss: 1.687259\n",
      "(Iteration 1601 / 9800) loss: 1.397112\n",
      "(Iteration 1621 / 9800) loss: 1.445749\n",
      "(Iteration 1641 / 9800) loss: 1.069371\n",
      "(Iteration 1661 / 9800) loss: 1.478389\n",
      "(Iteration 1681 / 9800) loss: 1.238313\n",
      "(Iteration 1701 / 9800) loss: 1.736258\n",
      "(Iteration 1721 / 9800) loss: 1.413835\n",
      "(Iteration 1741 / 9800) loss: 1.436884\n",
      "(Iteration 1761 / 9800) loss: 1.478697\n",
      "(Iteration 1781 / 9800) loss: 1.513124\n",
      "(Iteration 1801 / 9800) loss: 1.539456\n",
      "(Iteration 1821 / 9800) loss: 1.117370\n",
      "(Iteration 1841 / 9800) loss: 1.606114\n",
      "(Iteration 1861 / 9800) loss: 1.458941\n",
      "(Iteration 1881 / 9800) loss: 1.670295\n",
      "(Iteration 1901 / 9800) loss: 1.675310\n",
      "(Iteration 1921 / 9800) loss: 1.561432\n",
      "(Iteration 1941 / 9800) loss: 1.336988\n",
      "(Epoch 2 / 10) train acc: 0.551000; val_acc: 0.540000\n",
      "(Iteration 1961 / 9800) loss: 1.475469\n",
      "(Iteration 1981 / 9800) loss: 1.685217\n",
      "(Iteration 2001 / 9800) loss: 1.516707\n",
      "(Iteration 2021 / 9800) loss: 1.243012\n",
      "(Iteration 2041 / 9800) loss: 1.536620\n",
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      "(Iteration 2081 / 9800) loss: 1.513306\n",
      "(Iteration 2101 / 9800) loss: 1.745588\n",
      "(Iteration 2121 / 9800) loss: 1.569389\n",
      "(Iteration 2141 / 9800) loss: 1.336874\n",
      "(Iteration 2161 / 9800) loss: 1.553288\n",
      "(Iteration 2181 / 9800) loss: 1.295567\n",
      "(Iteration 2201 / 9800) loss: 1.383847\n",
      "(Iteration 2221 / 9800) loss: 1.340976\n",
      "(Iteration 2241 / 9800) loss: 1.270767\n",
      "(Iteration 2261 / 9800) loss: 1.390679\n",
      "(Iteration 2281 / 9800) loss: 1.447360\n",
      "(Iteration 2301 / 9800) loss: 1.791465\n",
      "(Iteration 2321 / 9800) loss: 1.147527\n",
      "(Iteration 2341 / 9800) loss: 1.381929\n",
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      "(Iteration 2381 / 9800) loss: 1.602965\n",
      "(Iteration 2401 / 9800) loss: 1.537054\n",
      "(Iteration 2421 / 9800) loss: 1.515816\n",
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      "(Iteration 2501 / 9800) loss: 1.083661\n",
      "(Iteration 2521 / 9800) loss: 1.448656\n",
      "(Iteration 2541 / 9800) loss: 1.141859\n",
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      "(Iteration 2581 / 9800) loss: 1.329625\n",
      "(Iteration 2601 / 9800) loss: 1.331611\n",
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      "(Iteration 2641 / 9800) loss: 1.212158\n",
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      "(Iteration 2681 / 9800) loss: 1.562883\n",
      "(Iteration 2701 / 9800) loss: 1.322882\n",
      "(Iteration 2721 / 9800) loss: 0.962277\n",
      "(Iteration 2741 / 9800) loss: 1.437086\n",
      "(Iteration 2761 / 9800) loss: 1.351344\n",
      "(Iteration 2781 / 9800) loss: 1.200690\n",
      "(Iteration 2801 / 9800) loss: 1.319568\n",
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      "(Iteration 2841 / 9800) loss: 1.458888\n",
      "(Iteration 2861 / 9800) loss: 1.032296\n",
      "(Iteration 2881 / 9800) loss: 1.259473\n",
      "(Iteration 2901 / 9800) loss: 1.257818\n",
      "(Iteration 2921 / 9800) loss: 1.390426\n",
      "(Epoch 3 / 10) train acc: 0.624000; val_acc: 0.578000\n",
      "(Iteration 2941 / 9800) loss: 1.314020\n",
      "(Iteration 2961 / 9800) loss: 1.149409\n",
      "(Iteration 2981 / 9800) loss: 1.044838\n",
      "(Iteration 3001 / 9800) loss: 1.297935\n",
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      "(Iteration 3181 / 9800) loss: 1.169529\n",
      "(Iteration 3201 / 9800) loss: 1.541700\n",
      "(Iteration 3221 / 9800) loss: 1.175477\n",
      "(Iteration 3241 / 9800) loss: 1.364353\n",
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      "(Iteration 3321 / 9800) loss: 0.755468\n",
      "(Iteration 3341 / 9800) loss: 1.254306\n",
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      "(Iteration 3481 / 9800) loss: 1.292845\n",
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      "(Iteration 3641 / 9800) loss: 1.368213\n",
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      "(Iteration 3681 / 9800) loss: 1.396994\n",
      "(Iteration 3701 / 9800) loss: 1.129754\n",
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      "(Iteration 3741 / 9800) loss: 1.091565\n",
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      "(Iteration 3801 / 9800) loss: 1.286283\n",
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      "(Iteration 3841 / 9800) loss: 1.004842\n",
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      "(Iteration 3881 / 9800) loss: 1.453433\n",
      "(Iteration 3901 / 9800) loss: 0.968912\n",
      "(Epoch 4 / 10) train acc: 0.633000; val_acc: 0.584000\n",
      "(Iteration 3921 / 9800) loss: 1.219011\n",
      "(Iteration 3941 / 9800) loss: 1.198370\n",
      "(Iteration 3961 / 9800) loss: 1.254945\n",
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      "(Iteration 4181 / 9800) loss: 0.977904\n",
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      "(Iteration 4741 / 9800) loss: 1.145998\n",
      "(Iteration 4761 / 9800) loss: 1.294625\n",
      "(Iteration 4781 / 9800) loss: 1.131389\n",
      "(Iteration 4801 / 9800) loss: 1.164881\n",
      "(Iteration 4821 / 9800) loss: 1.079552\n",
      "(Iteration 4841 / 9800) loss: 1.192538\n",
      "(Iteration 4861 / 9800) loss: 1.192761\n",
      "(Iteration 4881 / 9800) loss: 1.081850\n",
      "(Epoch 5 / 10) train acc: 0.619000; val_acc: 0.589000\n",
      "(Iteration 4901 / 9800) loss: 1.141651\n",
      "(Iteration 4921 / 9800) loss: 1.069416\n",
      "(Iteration 4941 / 9800) loss: 1.003472\n",
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      "(Iteration 4981 / 9800) loss: 1.192822\n",
      "(Iteration 5001 / 9800) loss: 1.157126\n",
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      "(Epoch 10 / 10) train acc: 0.686000; val_acc: 0.632000\n"
     ]
    }
   ],
   "source": [
    "# Train a really good model on CIFAR-10\n",
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, filter_size=3, num_filters=68, reg=0.002)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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